Feature selection method based on linear Twin Support Vector Machine

Abstract

Abstract: A new embedded feature selection method based on linear Twin Support Vector Machine（TWSVM） is proposed. It selects features during classifier construction by introducing a penalty term in the primal formulation of Twin Support Vector Machine. In the solving process, it utilizes alternating iterative optimization method to decompose the problem of solving the model into two sub-problems, namely the standard TWSVM optimization problem and the nonlinear constrained optimization problem about feature weight, and effectively solves the sub-problems respectively. The feature selection method is analyzed and compared on UCI datasets. Simulation results verify the proposed method is effective.

Key words: feature selection, Twin Support Vector Machine（TWSVM）, L1-norm, embedded methods

摘要： 提出了一种基于线性孪生支持向量机（TWSVM）的嵌入式特征选择方法。该方法在构造分类器的过程中，通过在TWSVM原有优化模型中引入一个惩罚项，来实现特征选择。在求解过程中，采用交替迭代优化方法将该模型求解问题分解成两个子问题来处理，即标准TWSVM优化问题和关于特征权重的非线性约束优化问题，并分别对子问题进行有效求解。在UCI数据集上对算法进行了仿真分析和比较，仿真结果验证了算法的有效性。

关键词: 特征选择, 孪生支持向量机, L1范数, 嵌入式方法

LI Xinbin, QIU Jiankun, HAN Song. Feature selection method based on linear Twin Support Vector Machine[J]. Computer Engineering and Applications, 2016, 52(15): 88-92.

李鑫滨，邱建坤，韩松. 基于线性孪生支持向量机的特征选择方法[J]. 计算机工程与应用, 2016, 52(15): 88-92.

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Feature selection method based on linear Twin Support Vector Machine

基于线性孪生支持向量机的特征选择方法

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