Communications of the ACM
Feature selection in data mining
Data mining
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
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)
A review of feature selection techniques in bioinformatics
Bioinformatics
A Practical Approach to Microarray Data Analysis
A Practical Approach to Microarray Data Analysis
A new dataset evaluation method based on category overlap
Computers in Biology and Medicine
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Derivation of an artificial gene to improve classification accuracy upon gene selection
Computational Biology and Chemistry
Computers in Biology and Medicine
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Feature selection is one of the most important issues in classification. Many filter and wrapper methods have been proposed. Here, we propose a new efficient feature selection method based on the R-value, which is a measure that is used to capture the overlapped areas among classes in a feature. Our strategy was to select features that have low overlapping areas among classes. Proposed idea is simple, but powerful for feature selection. The experiment results showed that the proposed method is better than previous typical methods in many cases. Accordingly, the proposed method can be used in combination with other feature selection methods.