Support vector classifier based on principal component analysis

Chen, Zheng; Jiao, Licheng; Li, Yongzhao

doi:10.1016/s1004-4132(08)60065-1

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…To solve the irregular packing problem, Gua et al proposed a packing algorithm based on the PCA methodology which resulted in an increased filling rate, decreased packing time, and increased packing number as compared to the MGA method [63]. Zheng et al proposed a PCA-based support vector classifier and noted an increased identification rate in their heart and adult data sets, as compared to the conventional support vector classifier [64]. Table 4 shows the previous research work conducted using the PCA and how it affected the recognition rate results.…”

Section: Principal Component Analysismentioning

confidence: 99%

A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

Fernandes

Ullah

2022

IEEE Access

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Over a few decades, a remarkable amount of research has been conducted in the field of speech signal processing particularly on deception detection for security applications. In this study, a comprehensive review on recent machine learning approaches using verbal and non-verbal features is presented for deception detection. A brief overview on different feature extraction techniques, the results of recognition rate, and computational time based on machine learning methods are summarized in a tabular format. In addition, numerous datasets used as primary sources of deception detection in the review articles are also presented in this work. Key findings from the reviewed articles are summarized and a few major issues related to deception detection approaches are examined. A statistical analysis which conducted by extracting the significant information from the eighty-eight (88) scientific papers over the last thirty (30) years are provided in this review paper. The results emphasize on the trends of research in deception detection as well as further research opportunities for researchers as a part of continuous progress.

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Section: Principal Component Analysismentioning

confidence: 99%

A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

Fernandes

Ullah

2022

IEEE Access

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“…It is based on structural risk minimization criteria, and its topology determined by the support vector. Therefore, it can overcome the shortcomings of artificial neural network based on empirical risk minimization criteria [7], and can solve the problem of small samples, nonlinearity, high dimension, etc. The Least Squares Support Vector Machine (LS-SVM) [8][9][10] is a new extension of the standard SVM, which can change inequality constraint of SVM to equality constraint.…”

Section: Introductionmentioning

confidence: 99%

Surface Quality Prediction of Atmospheric Pressure Plasma Arc Cleaning Based on Least Square Support Vector Machine

Yin

Dong

Meng

2013

AMR

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The theory of Least Squares Support Vector Machines was applied to metal surfaces cleaning by atmospheric pressure plasma arc. An intelligent predictive model of the non-linear relationship between cleaning quality and process parameters was established with the k-fold cross training of sample data. An orthogonal experiment was conducted to assess the effect of processing parameters on surface quality. The experimental results and predicted values show that the atmospheric pressure plasma arc (APPA) cleaning is effective in reducing considerably the amount of lubricant. Furthermore, it is feasible to apply LS-SVM in forecasting the cleaning quality and determining processing parameters, and the mean absolute percent error eMAPE between predictive value and experimental value of water contact angle is 6.09%. Otherwise, the eMAPE of working current is 4.46%.

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“…In this paper, firstly, we normalized the data, then we used principal component analysis [8] for dimensionality reduction, and finally classify the multi-class data.…”

Section: The Experimental Datamentioning

confidence: 99%

Breast Tissue Classification based on Electrical Impedance Spectroscopy

Liu¹,

Chang²,

Li³

2015

Proceedings of the 2015 International Conference on Industrial Technology and Management Science

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The physiopathology state of human breast tissue can be reflected by the electrical impedance spectral characteristics. In this paper, breast tissue classification based on the electrical impedance spectral characteristics was proposed for the breast disease diagnosis in the early stage. 9 breast tissue characteristics were obtained by measuring the electrical impedance spectra of the breast tissue which is collected in vitro and fresh, and then the breast tissue can be classified by the support vector machine. The experimental results show that this method can classify the breast tissue effectively with accuracy in more than 80%. Particularly, the classification accuracy of breast cancer and fatty tissue are close to 100%.

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Support vector classifier based on principal component analysis

Cited by 6 publications

References 9 publications

A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

Surface Quality Prediction of Atmospheric Pressure Plasma Arc Cleaning Based on Least Square Support Vector Machine

Breast Tissue Classification based on Electrical Impedance Spectroscopy

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