An Effective Feature Selection Scheme for Healthcare Data Classification Using Binary Particle Swarm Optimization

Yiyuan Chen, Yufeng Wang*, Liang Cao, Qun Jin

*この研究の対応する著者

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Feature selection (FS) is one of fundamental data processing techniques in various machine learning algorithms, especially for classification of healthcare data. However, it is a challenging issue due to the large search space. This paper proposed a confidence based and cost effective feature selection method using binary particle swarm optimization, CCFS. First, CCFS improves search effectiveness by developing a new updating mechanism, in which confidence of each feature is explicitly considered, including the correlation between feature and categories, and historically selected frequency of each feature. Second, the classification accuracy, the feature reduction ratio, and the feature cost are comprehensively incorporated into the design of the fitness function. The proposed method has been verified in UCI cancer classification dataset (Lung Cancer). The experimental result shows the effectiveness of the proposed method, in terms of accuracy and feature selection cost.

本文言語English
ホスト出版物のタイトルProceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ703-707
ページ数5
ISBN(電子版)9781538677438
DOI
出版ステータスPublished - 2018 12 26
イベント9th International Conference on Information Technology in Medicine and Education, ITME 2018 - Hangzhou, Zhejiang, China
継続期間: 2018 10 192018 10 21

出版物シリーズ

名前Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018

Conference

Conference9th International Conference on Information Technology in Medicine and Education, ITME 2018
国/地域China
CityHangzhou, Zhejiang
Period18/10/1918/10/21

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 医学(その他)
  • 情報システム
  • 健康情報学
  • 教育

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