Large Margin Feature Selection for Support Vector Machine

Feature selection is an preprocessing step in pattern analysis and machine learning. In this paper, we design a algorithm for feature subset. We present L1-norm regularization technique for sparse feature weight. Margin loss are introduced to evaluate features, and we employs gradient descent to search the optimal solution to maximize margin. The proposed technique is tested on UCI data sets. Compared with four margin based loss functions for SVM, the proposed technique is effective and efficient.

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A novel feature selection algorithm based on damping oscillation theory

PLoS ONE ◽

10.1371/journal.pone.0255307 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255307

Author(s):

Fujun Wang ◽

Xing Wang

Keyword(s):

Feature Selection ◽

Optimization Algorithm ◽

Euclidean Distance ◽

Oscillation Theory ◽

Feature Subset Selection ◽

Support Vector ◽

Data Sets ◽

Feature Subset ◽

Selection Algorithm ◽

Filter Model

Feature selection is an important task in big data analysis and information retrieval processing. It reduces the number of features by removing noise, extraneous data. In this paper, one feature subset selection algorithm based on damping oscillation theory and support vector machine classifier is proposed. This algorithm is called the Maximum Kendall coefficient Maximum Euclidean Distance Improved Gray Wolf Optimization algorithm (MKMDIGWO). In MKMDIGWO, first, a filter model based on Kendall coefficient and Euclidean distance is proposed, which is used to measure the correlation and redundancy of the candidate feature subset. Second, the wrapper model is an improved grey wolf optimization algorithm, in which its position update formula has been improved in order to achieve optimal results. Third, the filter model and the wrapper model are dynamically adjusted by the damping oscillation theory to achieve the effect of finding an optimal feature subset. Therefore, MKMDIGWO achieves both the efficiency of the filter model and the high precision of the wrapper model. Experimental results on five UCI public data sets and two microarray data sets have demonstrated the higher classification accuracy of the MKMDIGWO algorithm than that of other four state-of-the-art algorithms. The maximum ACC value of the MKMDIGWO algorithm is at least 0.5% higher than other algorithms on 10 data sets.

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Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines

Information Sciences ◽

10.1016/j.ins.2014.07.015 ◽

2014 ◽

Vol 286 ◽

pp. 228-246 ◽

Cited By ~ 143

Author(s):

Sebastián Maldonado ◽

Richard Weber ◽

Fazel Famili

Keyword(s):

Feature Selection ◽

Support Vector Machines ◽

Imbalanced Data ◽

High Dimensional ◽

Support Vector ◽

Data Sets ◽

Imbalanced Data Sets ◽

Vector Machines ◽

Selection For

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An Interior Point Method forL1/2-SVM and Application to Feature Selection in Classification

Journal of Applied Mathematics ◽

10.1155/2014/942520 ◽

2014 ◽

Vol 2014 ◽

pp. 1-16 ◽

Cited By ~ 2

Author(s):

Lan Yao ◽

Xiongji Zhang ◽

Dong-Hui Li ◽

Feng Zeng ◽

Haowen Chen

Keyword(s):

Feature Selection ◽

Interior Point ◽

Real Data ◽

Classification Performance ◽

Support Vector ◽

Interior Point Algorithm ◽

Lipschitz Optimization ◽

Regularization Technique ◽

Svm Model ◽

Selection For

This paper studies feature selection for support vector machine (SVM). By the use of theL1/2regularization technique, we propose a new modelL1/2-SVM. To solve this nonconvex and non-Lipschitz optimization problem, we first transform it into an equivalent quadratic constrained optimization model with linear objective function and then develop an interior point algorithm. We establish the convergence of the proposed algorithm. Our experiments with artificial data and real data demonstrate that theL1/2-SVM model works well and the proposed algorithm is more effective than some popular methods in selecting relevant features and improving classification performance.

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Application of Fuzzy Entropy to Improve Feature Selection for Defect Recognition Using Support Vector Machine in High Voltage Cable Joints

IEEE Transactions on Dielectrics and Electrical Insulation ◽

10.1109/tdei.2020.009055 ◽

2020 ◽

Vol 27 (6) ◽

pp. 2147-2155

Author(s):

Chien-Kuo Chang ◽

Bharath Kumar Boyanapalli ◽

Ruay-Nan Wu

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

High Voltage ◽

Fuzzy Entropy ◽

Support Vector ◽

Selection For ◽

Defect Recognition

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Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Sensors ◽

10.3390/s21051816 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1816

Author(s):

Hailun Xie ◽

Li Zhang ◽

Chee Peng Lim ◽

Yonghong Yu ◽

Han Liu

Keyword(s):

Feature Selection ◽

Particle Swarm ◽

Nonlinear Function ◽

Particle Swarm Optimisation ◽

Data Sets ◽

Discriminative Feature ◽

Advanced Search ◽

Selection For ◽

Selection Tasks ◽

New Strategies

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.

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