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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

CBFS: high performance feature selection algorithm based on feature clearness.

Minseok Seo1, Sejong Oh

  • 1Department of Nanobiomedical Science and WCU Research Center of Nanobiomedical Science, Dankook University, Cheonan, South Korea.

Plos One
|July 14, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new feature selection algorithm (CBFS) that enhances classification accuracy by prioritizing clear features. CBFS outperforms existing methods and is applicable to diverse data types like gene expression and text.

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Area of Science:

  • Computer Science
  • Machine Learning
  • Data Mining

Background:

  • Feature selection is crucial for improving classification accuracy and reducing processing time by identifying relevant features.
  • The challenge lies in effectively distinguishing useful features from irrelevant ones in datasets.

Purpose of the Study:

  • To develop a novel feature selection algorithm, CBFS (clearness-based feature selection), to enhance classification performance.
  • To introduce a metric, CScore, for quantifying feature clearness based on class separability.

Main Methods:

  • CBFS algorithm devised, utilizing feature clearness as a primary selection criterion.
  • CScore metric developed to measure feature clearness by assessing sample clustering around class centroids.
  • Exploration of combining CBFS with other algorithms for synergistic improvements in classification.

Main Results:

  • Experimental validation demonstrates CBFS's superiority over current feature selection algorithms, including FeaLect.
  • CBFS effectively identifies features that enhance classification accuracy.

Conclusions:

  • CBFS is a highly effective feature selection method, outperforming existing approaches.
  • The algorithm shows broad applicability in domains such as microarray gene selection, text categorization, and image classification.