A filter approach to feature selection based on mutual information

被引：0

作者：

Huang, Jinjie ^{[1
]}

Cai, Yunze ^{[1
]}

Xu, Xiaoming ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Automat, Dongchuan Rd 800, Shanghai 200240, Peoples R China

来源：

PROCEEDINGS OF THE FIFTH IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS, VOLS 1 AND 2 | 2006年

基金：

黑龙江省自然科学基金; 中国国家自然科学基金; 中国博士后科学基金;

关键词：

pattern classification; machine learning; feature selection; filter approach; mutual information;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In pattern recognition, feature selection aims to choose the smallest subset of features that is necessary and sufficient to describe the target concept. In this paper, a mutual information-based constructive criterion under arbitrary information distributions of input features is presented for feature selection. This criterion can capture both the relevance to the output classes and the redundancy with respect to the already,-selected features without any parameters like 8 in MIFS or MIFS-U methods to be preset. Furthermore, a modified greedy feature selection algorithm called MICC is proposed, and experimental results demonstrate the good performance of MICC on both synthetic and benchmark data sets.

引用

页码：84 / 89

页数：6

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