Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool.

Incremental support vector machine algorithm based on multi-kernel learning

A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to improve the performance of SVM. Simulation results indicate that the proposed algorithm can not only solve the model selection problem in SVM incremental learning, but also improve the classification or prediction precision.

Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

Numerous studies have demonstrated that human microRNAs(miRNAs)and diseases are associated and studies on the microRNA-disease association(MDA)have been conducted.We developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning(HSIC-MKL)to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases,and improve the model effect.We constructed three kernels in miRNA and disease space and conducted kernel fusion using HSIC-MKL.Link propagation uses matrix factorization and matrix approximation to effectively reduce computation and time costs.The results of the experiment show that the approach we proposed has a good effect,and,in some respects,exceeds what existing models can do.

Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine

Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.

Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being challenged.To address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone safety.We deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone data.By default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of themodel.To improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV data.Based on the above improvements,we create a novel anomaly detection strategy FastICA-TGAK-OCELM.The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)dataset.The experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.

Short-term Wind Power Prediction Based on Soft Margin Multiple Kernel Learning Method

For short-term wind power prediction,a soft margin multiple kernel learning(MKL)method is proposed.In order to improve the predictive effect of the MKL method for wind power,a kernel slack variable is introduced into each base kernel to solve the objective function.Two kinds of soft margin MKL methods based on hinge loss function and square hinge loss function can be obtained when hinge loss functions and square hinge loss functions are selected.The improved methods demonstrate good robustness and avoid the disadvantage of the hard margin MKL method which only selects a few base kernels and discards other useful kernels when solving the objective function,thereby achieving an effective yet sparse solution for the MKL method.In order to verify the effectiveness of the proposed method,the soft margin MKL method was applied to the second wind farm of Tianfeng from Xinjiang for short-term wind power single-step prediction,and the single-step and multi-step predictions of short-term wind power was also carried out using measured data provided by alberta electric system operator(AESO).Compared with the support vector machine(SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)methods as well as the SimpleMKL method under the same conditions,the experimental results demonstrate that the soft margin MKL method with different loss functions can efficiently achieve higher prediction accuracy and good generalization performance for short-term wind power prediction,which confirms the effectiveness of the method.

EEG-based Emotion Recognition Using Multiple Kernel Learning

Emotion recognition based on electroencephalography(EEG)has a wide range of applications and has great potential value,so it has received increasing attention from academia and industry in recent years.Meanwhile,multiple kernel learning(MKL)has also been favored by researchers for its data-driven convenience and high accuracy.However,there is little research on MKL in EEG-based emotion recognition.Therefore,this paper is dedicated to exploring the application of MKL methods in the field of EEG emotion recognition and promoting the application of MKL methods in EEG emotion recognition.Thus,we proposed a support vector machine(SVM)classifier based on the MKL algorithm EasyMKL to investigate the feasibility of MKL algorithms in EEG-based emotion recognition problems.We designed two data partition methods,random division to verify the validity of the MKL method and sequential division to simulate practical applications.Then,tri-categorization experiments were performed for neutral,negative and positive emotions based on a commonly used dataset,the Shanghai Jiao Tong University emotional EEG dataset(SEED).The average classification accuracies for random division and sequential division were 92.25%and 74.37%,respectively,which shows better classification performance than the traditional single kernel SVM.The final results show that the MKL method is obviously effective,and the application of MKL in EEG emotion recognition is worthy of further study.Through the analysis of the experimental results,we discovered that the simple mathematical operations of the features on the symmetrical electrodes could not effectively integrate the spatial information of the EEG signals to obtain better performance.It is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion recognition.In summary,this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EEG-based emotion recognition research.

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquire the 2D distribution of soot volume fraction, and an apparatus-independent yield sooting index (YSI) was experimentally obtained. Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel learning (BMKL) model. A high correlation coefficient (0.986) between measured YSIs and predicted values with the BMKL model was obtained, indicating that the BMKL model had a reliable and accurate predictive capacity for YSI values of surrogate fuels. The BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel, jet fuel, and biodiesel in terms of sooting tendency. Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogate fuels that concurrently contain hydrocarbon and oxygenated components and shows a satisfying matching level. During surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in terms of sooting tendency and ensure the optimal surrogate has a satisfying matching level of soot behaviors. Due to the high accuracy and resolution of YSI prediction, the BMKL model is also capable of providing distinguishing information of sooting tendency for surrogate design.

Kernel matrix learning with a general regularized risk functional criterion

Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.

Prediction of flyrock induced by mine blasting using a novel kernel-based extreme learning machine

Blasting is a common method of breaking rock in surface mines.Although the fragmentation with proper size is the main purpose,other undesirable effects such as flyrock are inevitable.This study is carried out to evaluate the capability of a novel kernel-based extreme learning machine algorithm,called kernel extreme learning machine(KELM),by which the flyrock distance(FRD) is predicted.Furthermore,the other three data-driven models including local weighted linear regression(LWLR),response surface methodology(RSM) and boosted regression tree(BRT) are also developed to validate the main model.A database gathered from three quarry sites in Malaysia is employed to construct the proposed models using 73 sets of spacing,burden,stemming length and powder factor data as inputs and FRD as target.Afterwards,the validity of the models is evaluated by comparing the corresponding values of some statistical metrics and validation tools.Finally,the results verify that the proposed KELM model on account of highest correlation coefficient(R) and lowest root mean square error(RMSE) is more computationally efficient,leading to better predictive capability compared to LWLR,RSM and BRT models for all data sets.

Dynamic model for predicting nitrogen oxide concentration at outlet of selective catalytic reduction denitrification system based on kernel extreme learning machine

To solve the increasing model complexity due to several input variables and large correlations under variable load conditions,a dynamic modeling method combining a kernel extreme learning machine(KELM)and principal component analysis(PCA)was proposed and applied to the prediction of nitrogen oxide(NO_(x))concentration at the outlet of a selective catalytic reduction(SCR)denitrification system.First,PCA is applied to the feature information extraction of input data,and the current and previous sequence values of the extracted information are used as the inputs of the KELM model to reflect the dynamic characteristics of the NO_(x)concentration at the SCR outlet.Then,the model takes the historical data of the NO_(x)concentration at the SCR outlet as the model input to improve its accuracy.Finally,an optimization algorithm is used to determine the optimal parameters of the model.Compared with the Gaussian process regression,long short-term memory,and convolutional neural network models,the prediction errors are reduced by approximately 78.4%,67.6%,and 59.3%,respectively.The results indicate that the proposed dynamic model structure is reliable and can accurately predict NO_(x)concentrations at the outlet of the SCR system.

Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.

Deep kernel extreme learning machine classifier based on the improved sparrow search algorithm

In the classification problem,deep kernel extreme learning machine(DKELM)has the characteristics of efficient processing and superior performance,but its parameters optimization is difficult.To improve the classification accuracy of DKELM,a DKELM algorithm optimized by the improved sparrow search algorithm(ISSA),named as ISSA-DKELM,is proposed in this paper.Aiming at the parameter selection problem of DKELM,the DKELM classifier is constructed by using the optimal parameters obtained by ISSA optimization.In order to make up for the shortcomings of the basic sparrow search algorithm(SSA),the chaotic transformation is first applied to initialize the sparrow position.Then,the position of the discoverer sparrow population is dynamically adjusted.A learning operator in the teaching-learning-based algorithm is fused to improve the position update operation of the joiners.Finally,the Gaussian mutation strategy is added in the later iteration of the algorithm to make the sparrow jump out of local optimum.The experimental results show that the proposed DKELM classifier is feasible and effective,and compared with other classification algorithms,the proposed DKELM algorithm aciheves better test accuracy.

Adaptive Metric Learning for Dimensionality Reduction

Finding a suitable space is one of the most critical problems for dimensionality reduction. Each space corresponds to a distance metric defined on the sample attributes, and thus finding a suitable space can be converted to develop an effective distance metric. Most existing dimensionality reduction methods use a fixed pre-specified distance metric. However, this easy treatment has some limitations in practice due to the fact the pre-specified metric is not going to warranty that the closest samples are the truly similar ones. In this paper, we present an adaptive metric learning method for dimensionality reduction, called AML. The adaptive metric learning model is developed by maximizing the difference of the distances between the data pairs in cannot-links and those in must-links. Different from many existing papers that use the traditional Euclidean distance, we use the more generalized l2,p-norm distance to reduce sensitivity to noise and outliers, which incorporates additional flexibility and adaptability due to the selection of appropriate p-values for different data sets. Moreover, considering traditional metric learning methods usually project samples into a linear subspace, which is overstrict. We extend the basic linear method to a more powerful nonlinear kernel case so that well capturing complex nonlinear relationship between data. To solve our objective, we have derived an efficient iterative algorithm. Extensive experiments for dimensionality reduction are provided to demonstrate the superiority of our method over state-of-the-art approaches.

An Intelligent Deep Learning Based Xception Model for Hyperspectral Image Analysis and Classification

Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly increased.Accurate classification of HSI becomes a critical process in the domain of hyperspectral data analysis.The massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground materials.Presently,deep learning(DL)models particularly,convolutional neural networks(CNNs)become useful for HSI classification owing to the effective feature representation and high performance.In this view,this paper introduces a new DL based Xception model for HSI analysis and classification,called Xcep-HSIC model.Initially,the presented model utilizes a feature relation map learning(FRML)to identify the relationship among the hyperspectral features and explore many features for improved classifier results.Next,the DL based Xception model is applied as a feature extractor to derive a useful set of features from the FRML map.In addition,kernel extreme learning machine(KELM)optimized by quantum-behaved particle swarm optimization(QPSO)is employed as a classification model,to identify the different set of class labels.An extensive set of simulations takes place on two benchmarks HSI dataset,namely Indian Pines and Pavia University dataset.The obtained results ensured the effective performance of the XcepHSIC technique over the existing methods by attaining a maximum accuracy of 94.32%and 92.67%on the applied India Pines and Pavia University dataset respectively.

Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches

It is important to have a reasonable estimation of sediment transport rate with respect to its significant role in the planning and management of water resources projects. The complicate nature of sediment transport in gravel-bed rivers causes inaccuracies of empirical formulas in the prediction of this phenomenon. Artificial intelligences as alternative approaches can provide solutions to such complex problems. The present study aimed at investigating the capability of kernel-based approaches in predicting total sediment loads and identification of influential parameters of total sediment transport. For this purpose, Gaussian process regression(GPR), Support vector machine(SVM) and kernel extreme learning machine(KELM) are applied to enhance the prediction level of total sediment loads in 19 mountain gravel-bed streams and rivers located in the United States. Several parameters based on two scenarios are investigated and consecutive predicted results are compared with some well-known formulas. Scenario 1 considers only hydraulic characteristics and on the other side, the second scenario was formed using hydraulic and sediment properties. The obtained results reveal that using the parameters of hydraulic conditions asinputs gives a good estimation of total sediment loads. Furthermore, it was revealed that KELM method with input parameters of Froude number(Fr), ratio of average velocity(V) to shear velocity(U*) and shields number(θ) yields a correlation coefficient(R) of 0.951, a Nash-Sutcliffe efficiency(NSE) of 0.903 and root mean squared error(RMSE) of 0.021 and indicates superior results compared with other methods. Performing sensitivity analysis showed that the ratio of average velocity to shear flow velocity and the Froude number are the most effective parameters in predicting total sediment loads of gravel-bed rivers.

A stacked multiple kernel support vector machine for blast inducedflyrock prediction

As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one ofthe most important issues induced by blasting operations, since the accurate prediction of which is crucial fordelineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets ofblasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stackedMK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve themodel performance by addressing the importance level of different features. For comparison purpose, 6 othermachine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), ArtificialNeural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validationprocess for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVMmodel achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95and 99.25 in training and testing phase, respectively.

复合地层小直径隧道掘进机掘进速度区间预测

合理准确预测隧道掘进机(tunnel boring machine,TBM)的掘进速度是实现TBM智能化控制的关键问题之一,复合地层小直径TBM施工的不确定性较常规地质条件更强,而传统预测方法对施工过程的不确定性考虑不足。在此通过引入区间预测方法,提出基于4种不同Bootstrap方法结合KELM-ANN模型的TBM掘进速度区间预测模型,并以南水北调安阳输水隧洞工程为例,选取142组工程实测数据验证区间预测模型的有效性。研究结果表明:基于Rademacher分布建立的模型预测结果优于其他3种方法,不仅可以得到较好的点预测结果,还可以构造出较为清晰可靠的区间将掘进速度实测值完全包络在内;随着置信水平的提高,区间可容纳的不确定性和风险也逐渐上升,通过变化区间宽度,能较好地量化和解释TBM施工过程中的不确定性因素对掘进速度的影响。研究结果可为TBM掘进性能预测和掘进参数优化提供参考。

Application of SABO-VMD-KELM in Fault Diagnosis of Wind Turbines

In order to improve the accuracy of wind turbine fault diagnosis,a wind turbine fault diagnosis method based on Subtraction-Average-Based Optimizer(SABO)optimized Variational Mode Decomposition(VMD)and Kernel Extreme Learning Machine(KELM)is proposed.Firstly,the SABO algorithm was used to optimize the VMD parameters and decompose the original signal to obtain the best modal components,and then the nine features were calculated to obtain the feature vectors.Secondly,the SABO algorithm was used to optimize the KELM parameters,and the training set and the test set were divided according to different proportions.The results were compared with the optimized model without SABO algorithm.The experimental results show that the fault diagnosis method of wind turbine based on SABO-VMD-KELM model can achieve fault diagnosis quickly and effectively,and has higher accuracy.

Model identification of continuous stirred tank reactor based on QKLMS algorithm

The continuous stirred tank reactor(CSTR)is one of the typical chemical processes.Aiming at its strong nonlinear characteristics,a quantized kernel least mean square(QKLMS)algorithm is proposed.The QKLMS algorithm is based on a simple online vector quantization technology instead of sparsification,which can compress the input or feature space and suppress the growth of the radial basis function(RBF)structure in the kernel learning algorithm.To verify the effectiveness of the algorithm,it is applied to the model identification of CSTR process to construct a nonlinear mapping relationship between coolant flow rate and product concentration.In additiion,the proposed algorithm is further compared with least squares support vector machine(LS-SVM),echo state network(ESN),extreme learning machine with kernels(KELM),etc.The experimental results show that the proposed algorithm has higher identification accuracy and better online learning ability under the same conditions.