Elements of information theory
Elements of information theory
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Feature selection with conditional mutual information maximin in text categorization
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Dependency and Correlation Analysis for Features
IEEE Transactions on Knowledge and Data Engineering
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Pattern Recognition Letters
Conditional mutual information based feature selection for classification task
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
An effective feature selection method using dynamic information criterion
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
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In real-world application, data is often represented by hundreds or thousands of features. Most of them, however, are redundant or irrelevant, and their existence may straightly lead to poor performance of learning algorithms. Hence, it is a compelling requisition for their practical applications to choose most salient features. Currently, a large number of feature selection methods using various strategies have been proposed. Among these methods, the mutual information ones have recently gained much more popularity. In this paper, a general criterion function for feature selector using mutual information is firstly introduced. This function can bring up-to-date selectors based on mutual information together under an unifying scheme. Then an experimental comparative study of eight typical filter mutual information based feature selection algorithms on thirty-three datasets is presented. We evaluate them from four essential aspects, and the experimental results show that none of these methods outperforms others significantly. Even so, the conditional mutual information feature selection algorithm dominates other methods on the whole, if training time is not a matter.