Elements of information theory
Elements of information theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
A Compact and Accurate Model for Classification
IEEE Transactions on Knowledge and Data Engineering
Consistency-based search in feature selection
Artificial Intelligence
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Mutual information-based feature extraction on the time-frequencyplane
IEEE Transactions on Signal Processing
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
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Feature selection plays an important role in pattern classification. In this paper, a hybrid genetic algorithm (HGA) is adopted to find a subset of the most relevant features. The approach utilizes an improved estimation of the conditional mutual information as an independent measure for feature ranking in the local search operations. It takes account of not only the relevance of the candidate feature to the output classes but also the redundancy between the candidate feature and the already-selected features. Thus, the ability of the HGA to search for the optimal subset of features has been greatly enhanced. Experimental results on a range of benchmark datasets demonstrate that the proposed method can usually find the excellent subset of features on which high classification accuracy is achieved.