This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a rand...This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.展开更多
Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-n...Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-nearest neighbor(KNN), and fuzzy KNN(FKNN), have difficulty in accurately predicting the stock trend(uptrend or downtrend) for a given date, a generalized Heronian mean(GHM) based FKNN predictor named GHM-FKNN was proposed.GHM-FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM-FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM-FKNN achieved the best performance with accuracy of 62.37% for AAPL, 58.25% for AMZN, and 64.10% for NFLX.展开更多
Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose t...Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose the disc space variation(DSV)fault degree of transformer winding,this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor(KNN)algorithmand the frequency response analysis(FRA)method.First,a laboratory winding model is used,and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding.Then,a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset.Second,ten different numerical indices are utilized to obtain features of FRA curves of faulted winding.Third,the 10-fold cross-validation method is employed to determine the optimal k-value of KNN.In addition,to improve the accuracy of the KNN model,a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions.After getting the most appropriate distance metric and kvalue,the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults.The identification accuracy rate of the proposed model is up to 98.30%.Finally,the performance of the model is presented by comparing with the support vector machine(SVM),SVM optimized by the particle swarmoptimization(PSO-SVM)method,and randomforest(RF).The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding.展开更多
The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes.Because of its operation,the application of this classification may be limited to problems with a cer...The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes.Because of its operation,the application of this classification may be limited to problems with a certain number of instances,particularly,when run time is a consideration.However,the classification of large amounts of data has become a fundamental task in many real-world applications.It is logical to scale the k-Nearest Neighbor method to large scale datasets.This paper proposes a new k-Nearest Neighbor classification method(KNN-CCL)which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts.The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters.The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets.Finally,sets of experiments are conducted on the UCI datasets.The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance.展开更多
As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring s...As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+).展开更多
Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats...Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.展开更多
Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting...Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm is proposed. Specifically, two screening layers based on shape similarity were introduced in K-nearest neighbor non-parametric regression method, and the forecasting results were output using the weighted averaging on the reciprocal values of the shape similarity distances and the most-similar-point distance adjustment method. According to the experimental results, the proposed algorithm has improved the predictive ability of the traditional K-nearest neighbor non-parametric regression method, and greatly enhanced the accuracy and real-time performance of short-term traffic flow forecasting.展开更多
On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feat...On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.展开更多
In this paper,we develop and apply K-Nearest Neighbor algorithm to propagation pathloss regression.The path loss models present the dependency of attenuation value on distance using machine learning algorithms based o...In this paper,we develop and apply K-Nearest Neighbor algorithm to propagation pathloss regression.The path loss models present the dependency of attenuation value on distance using machine learning algorithms based on the experimental data.The algorithm is performed by choosing k nearest points and training dataset to find the optimal k value.The proposed method is applied to impove and adjust pathloss model at 28 GHz in Keangnam area,Hanoi,Vietnam.The experiments in both line-of-sight and non-line-of-sight scenarios used many combinations of transmit and receive antennas at different transmit antenna heights and random locations of receive antenna have been carried out using Wireless Insite Software.The results have been compared with 3GPP and NYU Wireless Path Loss Models in order to verify the performance of the proposed approach.展开更多
Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically ...Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically modeling the actual scene, so that the hand-held full-band spectrum analyzer would be able to collect signal field strength values for indoor complex scenes. An improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression was proposed to predict the signal field strengths for the whole plane before and after being shield. Then the highest accuracy set of data could be picked out by comparison. The experimental results show that the improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression can scientifically and objectively predict the indoor complex scenes’ signal strength and evaluate the interference protection with high accuracy.展开更多
The increasing amount of user traffic on Internet discussion forums has led to a huge amount of unstructured natural language data in the form of user comments.Most modern recommendation systems rely on manual tagging...The increasing amount of user traffic on Internet discussion forums has led to a huge amount of unstructured natural language data in the form of user comments.Most modern recommendation systems rely on manual tagging,relying on administrators to label the features of a class,or story,which a user comment corresponds to.Another common approach is to use pre-trained word embeddings to compare class descriptions for textual similarity,then use a distance metric such as cosine similarity or Euclidean distance to find top k neighbors.However,neither approach is able to fully utilize this user-generated unstructured natural language data,reducing the scope of these recommendation systems.This paper studies the application of domain adaptation on a transformer for the set of user comments to be indexed,and the use of simple contrastive learning for the sentence transformer fine-tuning process to generate meaningful semantic embeddings for the various user comments that apply to each class.In order to match a query containing content from multiple user comments belonging to the same class,the construction of a subquery channel for computing class-level similarity is proposed.This channel uses query segmentation of the aggregate query into subqueries,performing k-nearest neighbors(KNN)search on each individual subquery.RecBERT achieves state-of-the-art performance,outperforming other state-of-the-art models in accuracy,precision,recall,and F1 score for classifying comments between four and eight classes,respectively.RecBERT outperforms the most precise state-of-the-art model(distilRoBERTa)in precision by 6.97%for matching comments between eight classes.展开更多
Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear mode...Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.展开更多
Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting inc...Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. Thereafter, using these imputed datasets as inputs, this study applies Support Vector Machine (SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach.展开更多
One of the most critical steps in medical health is the proper diagnosis of the disease.Dermatology is one of the most volatile and challenging fields in terms of diagnosis.Dermatologists often require further testing...One of the most critical steps in medical health is the proper diagnosis of the disease.Dermatology is one of the most volatile and challenging fields in terms of diagnosis.Dermatologists often require further testing,review of the patient’s history,and other data to ensure a proper diagnosis.Therefore,finding a method that can guarantee a proper trusted diagnosis quickly is essential.Several approaches have been developed over the years to facilitate the diagnosis based on machine learning.However,the developed systems lack certain properties,such as high accuracy.This study proposes a system developed in MATLAB that can identify skin lesions and classify them as normal or benign.The classification process is effectuated by implementing the K-nearest neighbor(KNN)approach to differentiate between normal skin and malignant skin lesions that imply pathology.KNN is used because it is time efficient and promises highly accurate results.The accuracy of the system reached 98%in classifying skin lesions.展开更多
在认知物联网(CIoT, cognitive internet of things)中,由于主用户(PU, primary user)与次级用户(SU,secondary user)之间的非合作特性,单独依靠传统的频谱感知技术判断频谱接入机会存在一定的不可靠性。作为一种重要的辅助信息,PU与SU...在认知物联网(CIoT, cognitive internet of things)中,由于主用户(PU, primary user)与次级用户(SU,secondary user)之间的非合作特性,单独依靠传统的频谱感知技术判断频谱接入机会存在一定的不可靠性。作为一种重要的辅助信息,PU与SU之间的相互位置信息可以协助判断授权频谱的二次接入可能性。提出了一种低复杂度的基于相邻关系的加权质心定位(NB-WCL, neighbor-based weighted centroid localization)算法,通过解决CIoT中SU的定位问题,从而完成CIoT中各个地理位置上是否能够进行频谱接入的决策。在理论层面分析了二维位置估计的均方根误差(RMSE, root mean square error)性能,通过仿真验证了通信半径、节点密集度、阴影影响、路径损失、连通性度量值以及发送数据次数等因素对于算法性能的影响。理论推导与实验结果表明,相对于传统的定位算法,所提方案为CIoT中的SU定位算法提供了更为强健和良好的定位误差性能,能够有效地增强认知物联网中用户频谱接入的可靠性。该方案可以作为认知物联网中的一种高效实用的定位感知方案。展开更多
The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationflow-ing over the network.The data will always remain under the threat of t...The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationflow-ing over the network.The data will always remain under the threat of technolo-gical suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights.In this paper,the authors pro-posed the HDTbNB(Hybrid Decision Tree-based Naïve Bayes)algorithm tofind the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS(Intrusion Detection System)and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes,Decision Tree,K-Nearest Neighbors and Logistic Regression over KDD 99 data-set.The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy,macro average,and weighted average.Thefindings were concluded as a percentage increase in accuracy,precision,sensitiv-ity,specificity,and a decrease in misclassification as 9.3%,6.4%,12.5%,5.2%and 81%.展开更多
Building an automatic fish recognition and detection system for largescale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species.However,it is quite diffi...Building an automatic fish recognition and detection system for largescale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species.However,it is quite difficult to build such systems owing to the lack of data imbalance problems and large number of classes.To solve these issues,we propose a transfer learning-based technique in which we use Efficient-Net,which is pre-trained on ImageNet dataset and fine-tuned on QuT Fish Database,which is a large scale dataset.Furthermore,prior to the activation layer,we use Global Average Pooling(GAP)instead of dense layer with the aim of averaging the results of predictions along with having more information compared to the dense layer.To check the validity of our model,we validate our model on the validation set which achieves satisfactory results.Also,for the localization task,we propose an architecture that consists of localization aware block,which captures localization information for better prediction and residual connections to handle the over-fitting problem.Actually,the residual connections help the layer to combine missing information with the relevant one.In addition,we use class weights and Focal Loss(FL)to handle class imbalance problems along with reducing false predictions.Actually,class weights assign less weights to classes having fewer instances and large weights to classes having more number of instances.During the localization,the qualitative assessment shows that we achieve 57%Mean Intersection Over Union(IoU)on testing data,and the classification results show 75%precision,70%recall,78%accuracy and 74%F1-Score for 468 fish species.展开更多
Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emer...Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms.However,there is no unsupervised interference signals recognition algorithm at present.In this paper,an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering(DDCC)is proposed.Specifically,in the first phase,four data augmentation strategies for interference signals are used in data-augmentation-based(DA-based)contrastive learning.In the second phase,the original dataset’s k-nearest neighbor set(KNNset)is designed in double dimensions contrastive learning.In addition,a dynamic entropy parameter strategy is proposed.The simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies;the feature dimensional contrastive learning in the second phase can improve the clustering purity;the dynamic entropy parameter strategy can improve the stability of DDCC effectively.The unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other algorithms.In particular,the clustering purity of our method is above 92%,SCAN’s is 81%,and the other three methods’are below 71%when jammingnoise-ratio(JNR)is−5 dB.In addition,our method is close to the supervised learning algorithm.展开更多
Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learni...Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.12002246 and No.52178301)Knowledge Innovation Program of Wuhan(Grant No.2022010801020357)+2 种基金the Science Research Foundation of Wuhan Institute of Technology(Grant No.K2021030)2020 annual Open Fund of Failure Mechanics&Engineering Disaster Prevention and Mitigation,Key Laboratory of Sichuan Province(Sichuan University)(Grant No.2020JDS0022)Open Research Fund Program of Hubei Provincial Key Laboratory of Chemical Equipment Intensification and Intrinsic Safety(Grant No.2019KA03)。
文摘This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.
基金Supported by the National Key Research and Development Program (No.2019YFA0707201)the Key Work Program of Institute of Scientific and Technical Information of China (No.ZD2022-01,ZD2023-07)。
文摘Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-nearest neighbor(KNN), and fuzzy KNN(FKNN), have difficulty in accurately predicting the stock trend(uptrend or downtrend) for a given date, a generalized Heronian mean(GHM) based FKNN predictor named GHM-FKNN was proposed.GHM-FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM-FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM-FKNN achieved the best performance with accuracy of 62.37% for AAPL, 58.25% for AMZN, and 64.10% for NFLX.
基金supported in part by Shaanxi Natural Science Foundation Project (2023-JC-QN-0438)in part by Fundamental Research Funds for the Central Universities (2452021050).
文摘Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose the disc space variation(DSV)fault degree of transformer winding,this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor(KNN)algorithmand the frequency response analysis(FRA)method.First,a laboratory winding model is used,and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding.Then,a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset.Second,ten different numerical indices are utilized to obtain features of FRA curves of faulted winding.Third,the 10-fold cross-validation method is employed to determine the optimal k-value of KNN.In addition,to improve the accuracy of the KNN model,a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions.After getting the most appropriate distance metric and kvalue,the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults.The identification accuracy rate of the proposed model is up to 98.30%.Finally,the performance of the model is presented by comparing with the support vector machine(SVM),SVM optimized by the particle swarmoptimization(PSO-SVM)method,and randomforest(RF).The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding.
基金The authors received no specific funding for this work.
文摘The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes.Because of its operation,the application of this classification may be limited to problems with a certain number of instances,particularly,when run time is a consideration.However,the classification of large amounts of data has become a fundamental task in many real-world applications.It is logical to scale the k-Nearest Neighbor method to large scale datasets.This paper proposes a new k-Nearest Neighbor classification method(KNN-CCL)which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts.The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters.The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets.Finally,sets of experiments are conducted on the UCI datasets.The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance.
文摘As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+).
文摘Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.
文摘Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm is proposed. Specifically, two screening layers based on shape similarity were introduced in K-nearest neighbor non-parametric regression method, and the forecasting results were output using the weighted averaging on the reciprocal values of the shape similarity distances and the most-similar-point distance adjustment method. According to the experimental results, the proposed algorithm has improved the predictive ability of the traditional K-nearest neighbor non-parametric regression method, and greatly enhanced the accuracy and real-time performance of short-term traffic flow forecasting.
基金supported by the Social Science Foundation of China under Grant No.17BGL231。
文摘On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.
基金This work is carried out in the framework of the project supported by the Department of Science and Technology of Kien Giang,Vietnam.The authors would like to thank them for supporting this research。
文摘In this paper,we develop and apply K-Nearest Neighbor algorithm to propagation pathloss regression.The path loss models present the dependency of attenuation value on distance using machine learning algorithms based on the experimental data.The algorithm is performed by choosing k nearest points and training dataset to find the optimal k value.The proposed method is applied to impove and adjust pathloss model at 28 GHz in Keangnam area,Hanoi,Vietnam.The experiments in both line-of-sight and non-line-of-sight scenarios used many combinations of transmit and receive antennas at different transmit antenna heights and random locations of receive antenna have been carried out using Wireless Insite Software.The results have been compared with 3GPP and NYU Wireless Path Loss Models in order to verify the performance of the proposed approach.
基金the National Natural Science Foundation of China under projects 61772150 and 61862012the Guangxi Key R&D Program under project AB17195025+5 种基金the Guangxi Natural Science Foundation under grants 2018GXNSFDA281054 and 2018GXNSFAA281232the National Cryptography Development Fund of China under project MMJJ20170217the Guangxi Science and Technology Base and Special Talents Program AD18281044the Innovation Project of GUET Graduate Education under project 2017YJCX46the Guangxi Young Teachers’ Basic Ability Improvement Program under Grant 2018KY0194the open program of Guangxi Key Laboratory of Cryptography and Information Security under projects GCIS201621 and GCIS201702.
文摘Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically modeling the actual scene, so that the hand-held full-band spectrum analyzer would be able to collect signal field strength values for indoor complex scenes. An improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression was proposed to predict the signal field strengths for the whole plane before and after being shield. Then the highest accuracy set of data could be picked out by comparison. The experimental results show that the improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression can scientifically and objectively predict the indoor complex scenes’ signal strength and evaluate the interference protection with high accuracy.
文摘The increasing amount of user traffic on Internet discussion forums has led to a huge amount of unstructured natural language data in the form of user comments.Most modern recommendation systems rely on manual tagging,relying on administrators to label the features of a class,or story,which a user comment corresponds to.Another common approach is to use pre-trained word embeddings to compare class descriptions for textual similarity,then use a distance metric such as cosine similarity or Euclidean distance to find top k neighbors.However,neither approach is able to fully utilize this user-generated unstructured natural language data,reducing the scope of these recommendation systems.This paper studies the application of domain adaptation on a transformer for the set of user comments to be indexed,and the use of simple contrastive learning for the sentence transformer fine-tuning process to generate meaningful semantic embeddings for the various user comments that apply to each class.In order to match a query containing content from multiple user comments belonging to the same class,the construction of a subquery channel for computing class-level similarity is proposed.This channel uses query segmentation of the aggregate query into subqueries,performing k-nearest neighbors(KNN)search on each individual subquery.RecBERT achieves state-of-the-art performance,outperforming other state-of-the-art models in accuracy,precision,recall,and F1 score for classifying comments between four and eight classes,respectively.RecBERT outperforms the most precise state-of-the-art model(distilRoBERTa)in precision by 6.97%for matching comments between eight classes.
文摘Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.
文摘Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. Thereafter, using these imputed datasets as inputs, this study applies Support Vector Machine (SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach.
文摘One of the most critical steps in medical health is the proper diagnosis of the disease.Dermatology is one of the most volatile and challenging fields in terms of diagnosis.Dermatologists often require further testing,review of the patient’s history,and other data to ensure a proper diagnosis.Therefore,finding a method that can guarantee a proper trusted diagnosis quickly is essential.Several approaches have been developed over the years to facilitate the diagnosis based on machine learning.However,the developed systems lack certain properties,such as high accuracy.This study proposes a system developed in MATLAB that can identify skin lesions and classify them as normal or benign.The classification process is effectuated by implementing the K-nearest neighbor(KNN)approach to differentiate between normal skin and malignant skin lesions that imply pathology.KNN is used because it is time efficient and promises highly accurate results.The accuracy of the system reached 98%in classifying skin lesions.
文摘The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationflow-ing over the network.The data will always remain under the threat of technolo-gical suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights.In this paper,the authors pro-posed the HDTbNB(Hybrid Decision Tree-based Naïve Bayes)algorithm tofind the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS(Intrusion Detection System)and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes,Decision Tree,K-Nearest Neighbors and Logistic Regression over KDD 99 data-set.The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy,macro average,and weighted average.Thefindings were concluded as a percentage increase in accuracy,precision,sensitiv-ity,specificity,and a decrease in misclassification as 9.3%,6.4%,12.5%,5.2%and 81%.
基金Zamil S.Alzamil would like to thank Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2022-172.
文摘Building an automatic fish recognition and detection system for largescale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species.However,it is quite difficult to build such systems owing to the lack of data imbalance problems and large number of classes.To solve these issues,we propose a transfer learning-based technique in which we use Efficient-Net,which is pre-trained on ImageNet dataset and fine-tuned on QuT Fish Database,which is a large scale dataset.Furthermore,prior to the activation layer,we use Global Average Pooling(GAP)instead of dense layer with the aim of averaging the results of predictions along with having more information compared to the dense layer.To check the validity of our model,we validate our model on the validation set which achieves satisfactory results.Also,for the localization task,we propose an architecture that consists of localization aware block,which captures localization information for better prediction and residual connections to handle the over-fitting problem.Actually,the residual connections help the layer to combine missing information with the relevant one.In addition,we use class weights and Focal Loss(FL)to handle class imbalance problems along with reducing false predictions.Actually,class weights assign less weights to classes having fewer instances and large weights to classes having more number of instances.During the localization,the qualitative assessment shows that we achieve 57%Mean Intersection Over Union(IoU)on testing data,and the classification results show 75%precision,70%recall,78%accuracy and 74%F1-Score for 468 fish species.
基金This research was supported by the National Natural Science Foundation of China under Grant No.U19B2016.,and Zhejiang Provincial Key Lab of Data Storage and Transmission Technology,Hangzhou Dianzi University.
文摘Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms.However,there is no unsupervised interference signals recognition algorithm at present.In this paper,an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering(DDCC)is proposed.Specifically,in the first phase,four data augmentation strategies for interference signals are used in data-augmentation-based(DA-based)contrastive learning.In the second phase,the original dataset’s k-nearest neighbor set(KNNset)is designed in double dimensions contrastive learning.In addition,a dynamic entropy parameter strategy is proposed.The simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies;the feature dimensional contrastive learning in the second phase can improve the clustering purity;the dynamic entropy parameter strategy can improve the stability of DDCC effectively.The unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other algorithms.In particular,the clustering purity of our method is above 92%,SCAN’s is 81%,and the other three methods’are below 71%when jammingnoise-ratio(JNR)is−5 dB.In addition,our method is close to the supervised learning algorithm.
基金Taif University Researchers Supporting Project Number(TURSP-2020/73)Taif University,Taif,Saudi Arabia.
文摘Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.