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

Yiyuan Chen, Yufeng Wang, Liang Cao, Qun Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages703-707
Number of pages5
ISBN (Electronic)9781538677438
DOIs
Publication statusPublished - 2018 Dec 26
Event9th International Conference on Information Technology in Medicine and Education, ITME 2018 - Hangzhou, Zhejiang, China
Duration: 2018 Oct 192018 Oct 21

Publication series

NameProceedings - 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
CountryChina
CityHangzhou, Zhejiang
Period18/10/1918/10/21

Keywords

  • Binary Particle Swarm Optimization
  • Feature selection
  • Healthcare data classification
  • Swarm intelligence

ASJC Scopus subject areas

  • Computer Science Applications
  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Education

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