Elements of information theory
Elements of information theory
Axiomatic Approach to Feature Subset Selection Based on Relevance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: concepts and techniques
Data mining: concepts and techniques
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric selection of input variables for connectionist learning
Nonparametric selection of input variables for connectionist learning
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Pattern Recognition Letters
A parameterless feature ranking algorithm based on MI
Neurocomputing
Non-linear variable selection for artificial neural networks using partial mutual information
Environmental Modelling & Software
Expert Systems with Applications: An International Journal
Feature selection with dynamic mutual information
Pattern Recognition
Expert Systems with Applications: An International Journal
Correntropy based feature selection using binary projection
Pattern Recognition
Feature subset selection with cumulate conditional mutual information minimization
Expert Systems with Applications: An International Journal
An effective feature selection scheme via genetic algorithm using mutual information
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Feature selection in SVM based on the hybrid of enhanced genetic algorithm and mutual information
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Divergence-based feature selection for separate classes
Neurocomputing
Feature selection using dynamic weights for classification
Knowledge-Based Systems
Robust feature selection based on regularized brownboost loss
Knowledge-Based Systems
A novel feature selection method and its application
Journal of Intelligent Information Systems
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This article proposes a novel mutual information-based feature selection scheme. In this scheme, the mutual information is estimated directly in an effective way even when one is handling a relative small data set. At the same time, the computation efficiency of the mutual information estimation is improved by proposing a supervised data compression algorithm. With these contributions, the proposed feature selection scheme is able to effectively identify the salience features. The proposed methodology is compared with the related study through applying to different classification problems in which the number of features ranged from less than 10 to over 12,600. The presented results are very promising and corroborate the contributions of the proposed methodology.