关键词: 特征选择, 高维数据, 稳定性, 1-范数支持向量机, 集成

Abstract: Feature selection is one of the key issues in the field of machine learning and pattern recognition. With pattern recognition and data mining becoming increasingly deeper, the target of research becoming more and more complex and the dimension of feature becoming higher and higher, the stability of feature selection is particularly important. Based on the sparse SVM (support vector machine) model, this paper analyzes L1SVM (1-norm support vector machine), applies this method to feature selection on high-dimensional data and integrates the results of feature selection according to ensemble learning principle of feature selection. Moreover, the paper designs a new stability measure for high-dimensional data. The experimental results on the gene expression data demonstrate that ensemble feature selection is able to effectively improve the stability of feature selection.

Key words: feature selection, high-dimensional data, stability, 1-norm support vector machine (L1SVM), ensemble