Elements of information theory
Elements of information theory
Saliency, Scale and Image Description
International Journal of Computer Vision
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
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
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Object Recognition with Informative Features and Linear Classification
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Dependency and Correlation Analysis for Features
IEEE Transactions on Knowledge and Data Engineering
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
On the Feature Selection Criterion Based on an Approximation of Multidimensional Mutual Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Feature selection using hierarchical feature clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
Conditional infomax learning: an integrated framework for feature extraction and fusion
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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Feature selection is one of the fundamental problems in pattern recognition and data mining. A popular and effective approach to feature selection is based on information theory, namely the mutual information of features and class variable. In this paper we compare eight different mutual information-based feature selection methods. Based on the analysis of the comparison results, we propose a new mutual information-based feature selection method. By taking into account both the class-dependent and class-independent correlation among features, the proposed method selects a less redundant and more informative set of features. The advantage of the proposed method over other methods is demonstrated by the results of experiments on UCI datasets (Asuncion and Newman, 2010 [1]) and object recognition.