Electrostatic Field Feature Selection Technique for Breakdown Voltage Prediction of Sphere Gaps Using Support Vector Regression

Human-computer intelligent interaction (HCII) is a rising field of science that aims to refine and enhance the interaction between computer and human. Since emotion plays a vital role in human daily life, the ability of computer to interpret and response to human emotion is a crucial element for future intelligent system. Accordingly, several studies have been conducted to recognise human emotion using different technique such as facial expression, speech, galvanic skin response (GSR), or heart rate (HR). However, such techniques have problems mainly in terms of credibility and reliability as people can fake their feeling and response. Electroencephalogram (EEG) on the other has shown to be a very effective way in recognising human emotion as this technique records the brain activity of human and they can hardly be deceived by voluntary control. Regardless the popularity of EEG in recognizing human emotion, this study field is relatively challenging as EEG signal is nonlinear, involves myriad factors and chaotic in nature. These issues have led to high dimensional problem and poor classification results. To address such problems, this study has proposed a novel computational model, which consist of three main stages, namely a) feature extraction; b) feature selection and c) classifier. Discrete wavelet packet transform (DWPT) has been used to extract EEG signals feature and ultimately 204,800 features from 32 subject-independent have been obtained. Meanwhile, Genetic Algorithm (GA) and Least squares support vector machine (LS-SVM) have been used as a feature selection technique and classifier respectively. This computational model is tested on the common DEAP pre-processed EEG dataset in order to classify three levels of valence and arousal. The empirical results have shown that the proposed GA-LSSVM, has improved the classification results to 49.22% and 54.83% for valence and arousal respectively, whereas is it observed that 46.33% of valence and 48.30% of arousal classification were achieved when no feature selection technique is applied on the identical classifier

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Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis

Sensors ◽

10.3390/s20236742 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6742

Author(s):

Harsh S. Dhiman ◽

Dipankar Deb ◽

James Carroll ◽

Vlad Muresan ◽

Mihaela-Ligia Unguresan

Keyword(s):

Feature Selection ◽

Support Vector Regression ◽

Condition Monitoring ◽

Regression Models ◽

Point Of View ◽

Support Vector ◽

Twin Support Vector Regression ◽

Feature Selection Technique ◽

Wind Turbine Gearbox ◽

Statistical Point

The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold–Mariano and Durbin–Watson tests are carried out to establish the robustness of the tested models.

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An Enhanced Hybrid Feature Selection Technique using Term Frequency-Inverse Document Frequency and Support Vector Machine-Recursive Feature Elimination for Sentiment Classification

IEEE Access ◽

10.1109/access.2021.3069001 ◽

2021 ◽

pp. 1-1

Author(s):

Nur Syafiqah Mohd Nafis ◽

Suryanti Awang

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Sentiment Classification ◽

Recursive Feature Elimination ◽

Support Vector ◽

Feature Selection Technique ◽

Inverse Document Frequency ◽

Selection Technique ◽

Term Frequency ◽

Document Frequency

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Predicting Tourists’ Behavior Of Virtual Museum Using Support Vector Machine With Feature Selection Technique

2018 International Conference on Machine Learning and Cybernetics (ICMLC) ◽

10.1109/icmlc.2018.8526959 ◽

2018 ◽

Author(s):

Krit Sriporn ◽

Cheng-Fa Tsai

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Support Vector ◽

Virtual Museum ◽

Feature Selection Technique ◽

Selection Technique

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Classification of Gene Expression Data using Efficient Feature Selection Technique and Resampling Method

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e7816.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 406-414

Keyword(s):

Gene Expression ◽

Feature Selection ◽

Gene Expression Data ◽

Classification Model ◽

Support Vector ◽

Expression Data ◽

Feature Selection Technique ◽

Selection Technique ◽

Resampling Method

Microarray technology has been developed as one of the powerful tools that have attracted many researchers to analyze gene expression level for a given organism. It has been observed that gene expression data have very large (in terms of thousands) of features and less number of samples (in terms of hundreds). This characteristic makes difficult to do an analysis of gene expression data. Hence efficient feature selection technique must be applied before we go for any kind of analysis. Feature selection plays a vital role in the classification of gene expression data. There are several feature selection techniques have been induced in this field. But Support Vector Machine with Recursive Feature Elimination (SVM-RFE) has been proven as the promising feature selection methods among others. SVM-RFE ranks the genes (features) by training the SVM classification model and with the combination of RFE method key genes are selected. Huge time consumption is the main issue of SVM-RFE. We introduced an efficient implementation of linier SVM to overcome this problem and improved the RFE with variable step size. Then, combined method was used for selecting informative genes. Effective resampling method is proposed to preprocess the datasets. This is used to make the distribution of samples balanced, which gives more reliable classification results. In this paper, we have also studied the applicability of common classifiers. Detailed experiments are conducted on four commonly used microarray gene expression datasets. The results show that the proposed method comparable classification performance

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Infinite Latent Feature Selection Technique for Hyperspectral Image Classification

Jurnal Elektronika dan Telekomunikasi ◽

10.14203/jet.v19.32-37 ◽

2019 ◽

Vol 19 (1) ◽

pp. 32

Author(s):

Tajul Miftahushudur ◽

Chaeriah Bin Ali Wael ◽

Teguh Praludi

Keyword(s):

Feature Selection ◽

Hyperspectral Image ◽

Support Vector ◽

Machine Learning Technique ◽

Feature Selection Technique ◽

Selection Technique ◽

Improve Accuracy ◽

Learning Technique ◽

Image Format ◽

Better Than

The classification process is one of the most crucial processes in hyperspectral imaging. One of the limitations in classification process using machine learning technique is its complexities, where hyperspectral image format has a thousand band that can be used as a feature for learning purpose. This paper presents a comparison between two feature selection technique based on probability approach that not only can tackle the problem, but also improve accuracy. Infinite Latent Feature Selection (ILFS) and Relief Techniques are implemented in a hyperspectral image to select the most important feature or band before applied in Support Vector Machine (SVM). The result showed ILFS technique can improve classification accuracy better than Relief (92.21% vs. 88.10%). However, Relief can extract less feature to reach its best accuracy with only 6 features compared with ILFS with 9.

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