Feature selection based on information theory, consistency and separability indices

Two new feature selection methods are introduced, the first based on separability criterion, the second on a consistency index that includes interactions between the selected subsets of features. Comparison of accuracy was made against information-theory based selection methods on several datasets t...

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Bibliographic Details
Main Authors: Duch, W., Grabczewski, K., Winiarski, T., Biesiada, J., Kachel, A.
Format: Conference Proceeding
Language:English
Subjects:
Bioinformatics
Chemistry
Data mining
Filtering
Genetic communication
Humans
Informatics
Information theory
Nearest neighbor searches
Proteins
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Summary:Two new feature selection methods are introduced, the first based on separability criterion, the second on a consistency index that includes interactions between the selected subsets of features. Comparison of accuracy was made against information-theory based selection methods on several datasets training neurofuzzy and nearest neighbor methods on various subsets of selected features. Methods based on separability seem to be most promising.
DOI:10.1109/ICONIP.2002.1199014