Active learning accelerated Monte-Carlo simulation based on the modified K-nearest neighbors algorithm and its application to reliability estimations

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.

GHM-FKNN:a generalized Heronian mean based fuzzy k-nearest neighbor classifier for the stock trend prediction

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.

Diagnosis of Disc Space Variation Fault Degree of Transformer Winding Based on K-Nearest Neighbor Algorithm

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.

基于不规则区域划分方法的k-Nearest Neighbor查询算法

随着越来越多的数据累积,对数据处理能力和分析能力的要求也越来越高.传统k-Nearest Neighbor(k NN)查询算法由于其容易导致计算负载整体不均衡的规则区域划分方法及其单个进程或单台计算机运行环境的较低数据处理能力.本文提出并详细介绍了一种基于不规则区域划分方法的改进型k NN查询算法,并利用对大规模数据集进行分布式并行计算的模型Map Reduce对该算法加以实现.实验结果与分析表明,Map Reduce框架下基于不规则区域划分方法的k NN查询算法可以获得较高的数据处理效率,并可以较好的支持大数据环境下数据的高效查询.

基于K-Nearest Neighbor和神经网络的糖尿病分类研究

为实现糖尿病的早期筛查,提高对糖尿病分类的准确度,在研究有关糖尿病危险因素的基础上,增加糖化血红蛋白作为糖尿病早期筛查的特征之一。研究中选取与人类最为相似的食蟹猴作为研究对象,利用年龄、血压、腹围、BMI、糖化血红蛋白以及空腹血糖作为特征输入,将正常、糖尿病前期和糖尿病作为类别输出,利用K-Nearest Neighbor(KNN)和神经网络两种方法对其分类。发现在增加糖化血红蛋白作为分类特征之一时,KNN(K=3)和神经网络的分类准确率分别为81.8%和92.6%,明显高于没有这一特征时的准确率(68.1%和89.7%),KNN和神经网络都可以对食蟹猴数据进行分类和识别,起到早期筛查作用。

Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico

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+).

Real-Time Spreading Thickness Monitoring of High-core Rockfill Dam Based on K-nearest Neighbor Algorithm

During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the K-nearest neighbor algorithm. Taking the LHK core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface.

Pruned fuzzy K-nearest neighbor classifier for beat classification

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.

A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm

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.

Computational Intelligence Prediction Model Integrating Empirical Mode Decomposition,Principal Component Analysis,and Weighted k-Nearest Neighbor

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.

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.

Condition Monitoring of Roller Bearing by K-star Classifier andK-nearest Neighborhood Classifier Using Sound Signal

Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.

Propagation Path Loss Models at 28 GHz Using K-Nearest Neighbor Algorithm

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.

Wireless Communication Signal Strength Prediction Method Based on the K-nearest Neighbor Algorithm

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.

Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm

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.

RecBERT:Semantic recommendation engine with large language model enhanced query segmentation for k-nearest neighbors ranking retrieval

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.

Consistency of the k-Nearest Neighbor Classifier for Spatially Dependent Data

The purpose of this paper is to investigate the k-nearest neighbor classification rule for spatially dependent data.Some spatial mixing conditions are considered,and under such spatial structures,the well known k-nearest neighbor rule is suggested to classify spatial data.We established consistency and strong consistency of the classifier under mild assumptions.Our main results extend the consistency result in the i.i.d.case to the spatial case.

基于沙地猫群优化–最小二乘支持向量机的动态NOx排放预测

针对火电机组频繁调峰导致机组燃烧状态不稳,进而导致锅炉出口NOx浓度波动范围大的问题,提出一种基于沙地猫群优化(sand cat sarm optimization,SCSO)的最小二乘支持向量机(leastsquaressupportvectormachine,LSSVM) NOx动态预测模型。首先利用k近邻互信息计算时间延迟的同时筛选辅助变量。然后,基于SCSO算法进行输入变量阶次的选择。使用包含辅助变量时间延迟和阶次的信息作为模型的输入,SCSO算法优化最小二乘支持向量机参数,建立动态NOx排放最小二乘支持向量机预测模型(SCSO-LSSVM动态软测量模型)。最后将模型与未加入迟延的LSSVM模型,加入迟延的LSSVM模型和粒子群优化算法(particle swarm optimization,PSO)优化最小二乘支持向量机参数的动态软测量模型进行对比验证。结果表明,相较于其他模型,该文建立SCSO-LSSVM动态软测量模型均方根误差、平均绝对误差、平均绝对误差最小,预测精度最高,而且在NOx浓度剧烈波动时也能够较好地预测NOx浓度,具有很好的动态特性。

Outsmarting Android Malware with Cutting-Edge Feature Engineering and Machine Learning Techniques

The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world.Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false positives.This meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in malwaredetection.The proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data accuracy.The Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction model.This study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert opinion.The proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,respectively.The empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both datasets.This approach has substantial potential for real-world applications and can serve asa valuable tool for preventing the spread of Androidmalware and enhancing mobile device security.