CCFS: A Confidence-Based Cost-Effective Feature Selection Scheme for Healthcare Data Classification

Yiyuan Chen, Yufeng Wang, Liang Cao, Qun Jin

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Feature selection (FS) is one of the 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. Binary Particle Swarm Optimization (BPSO) is an efficient evolutionary computation technique, and has been widely used in FS. In this paper, we proposed a Confidence-based and Cost-effective feature selection (CCFS) method using BPSO to improve the performance of healthcare data classification. Specifically, first, CCFS improves search effectiveness by developing a new updating mechanism that designs the feature confidence to explicitly take into account the fine-grained impact of each dimension in the particle on the classification performance. The feature confidence is composed of two measurements: the correlation between feature and categories, and historically selected frequency of each feature. Second, considering the fact that the acquisition costs of different features are naturally different, especially for medical data, and should be fully taken into account in practical applications, besides the classification performance, the feature cost and the feature reduction ratio are comprehensively incorporated into the design of fitness function. The proposed method has been verified in various UCI public datasets and compared with various benchmark schemes. The thoroughly experimental results show the effectiveness of the proposed method, in terms of accuracy and feature selection cost.

Original languageEnglish
Article number8662586
Pages (from-to)902-911
Number of pages10
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume18
Issue number3
DOIs
Publication statusPublished - 2021 May 1

Keywords

  • binary particle swarm optimization
  • Data classification
  • feature selection
  • swarm intelligence

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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