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CBFS: High Performance Feature Selection Algorithm Based on Feature Clearness

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  • Minseok Seo
  • Sejong Oh

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.

Suggested Citation

  • Minseok Seo & Sejong Oh, 2012. "CBFS: High Performance Feature Selection Algorithm Based on Feature Clearness," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
    • Handle: RePEc:plo:pone00:0040419
    DOI: 10.1371/journal.pone.0040419
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    References listed on IDEAS

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    1. Yujin Hoshida & Jean-Philippe Brunet & Pablo Tamayo & Todd R Golub & Jill P Mesirov, 2007. "Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets," PLOS ONE, Public Library of Science, vol. 2(11), pages 1-8, November.
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    Cited by:

    1. Kwondo Kim & Minseok Seo & Hyunsung Kang & Seoae Cho & Heebal Kim & Kang-Seok Seo, 2015. "Application of LogitBoost Classifier for Traceability Using SNP Chip Data," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-16, October.

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