期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 28, 期 9, 页码 2508-2521出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2016.2563436
关键词
Feature selection; feature reduction; text categorization; Kullback-Leibler divergence; Jeffreys divergence; information gain
类别
资金
- US National Science Foundation [ECCS 1053717, CCF 1439011]
- Army Research Office [W911NF-12-1-0378]
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1053717] Funding Source: National Science Foundation
Automated feature selection is important for text categorization to reduce feature size and to speed up learning process of classifiers. In this paper, we present a novel and efficient feature selection framework based on the Information Theory, which aims to rank the features with their discriminative capacity for classification. We first revisit two information measures: Kullback-Leibler divergence and Jeffreys divergence for binary hypothesis testing, and analyze their asymptotic properties relating to type I and type II errors of a Bayesian classifier. We then introduce a new divergence measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure multi-distribution divergence for multi-class classification. Based on the JMH-divergence, we develop two efficient feature selection methods, termed maximum discrimination (MD) and MD - chi(2) methods, for text categorization. The promising results of extensive experiments demonstrate the effectiveness of the proposed approaches.
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