Title :
A Filter Approach to Feature Selection Based on Mutual Information
Author :
Huang, Jinjie ; Cai, Yunze ; Xu, Xiaoming
Author_Institution :
Dept. of Autom., Shanghai Jiao Tong Univ.
Abstract :
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 beta 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
Keywords :
feature extraction; filtering theory; greedy algorithms; learning (artificial intelligence); pattern classification; MICC; filter approach; greedy feature selection algorithm; machine learning; mutual information-based constructive criterion; pattern classification; pattern recognition; Computational complexity; Concrete; Information filtering; Information filters; Machine learning; Machine learning algorithms; Mutual information; Noise measurement; Pattern classification; Pattern recognition; Pattern classification; feature selection; filter approach; machine learning; mutual information;
Conference_Titel :
Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-0475-4
DOI :
10.1109/COGINF.2006.365681
