CBFS: High Performance Feature Selection Algorithm Based on Feature Clearness

Minseok Seo and Sejong Oh

PLOS ONE, 2012, vol. 7, issue 7, 1-10

Abstract: Background: The goal of feature selection is to select useful features and simultaneously exclude garbage features from a given dataset for classification purposes. This is expected to bring reduction of processing time and improvement of classification accuracy. Methodology: In this study, we devised a new feature selection algorithm (CBFS) based on clearness of features. Feature clearness expresses separability among classes in a feature. Highly clear features contribute towards obtaining high classification accuracy. CScore is a measure to score clearness of each feature and is based on clustered samples to centroid of classes in a feature. We also suggest combining CBFS and other algorithms to improve classification accuracy. Conclusions/Significance: From the experiment we confirm that CBFS is more excellent than up-to-date feature selection algorithms including FeaLect. CBFS can be applied to microarray gene selection, text categorization, and image classification.

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0040419

DOI: 10.1371/journal.pone.0040419

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