Feature selection of high-dimensional biomedical data using improved SFLA for disease diagnosis

Yongqiang Dai*, Bin Hu, Yun Su, Chengsheng Mao, Jing Chen, Xiaowei Zhang, Philip Moore, Lixin Xu, Hanshu Cai

*Corresponding author for this work

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

13 Scopus citations

Abstract

High-dimensional biomedical datasets contain thousands of features used in molecular disease diagnosis, however many irrelevant or weak correlation features influence the predictive accuracy. Feature selection algorithms enable classification techniques to accurately identify patterns in the features and find a feature subset from an original set of features without reducing the predictive classification accuracy while reducing the computational overhead in data mining. In this paper we present an improved shuffled frog leaping algorithm (ISFLA) which explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant features in high-dimensional biomedical data. Evaluation employs the K-nearest neighbour approach and a comparative analysis with a genetic algorithm, particle swarm optimization and the shuffled frog leaping algorithm shows that our improved algorithm achieves improvements in the identification of relevant subsets and in classification accuracy.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Editorslng. Matthieu Schapranow, Jiayu Zhou, Xiaohua Tony Hu, Bin Ma, Sanguthevar Rajasekaran, Satoru Miyano, Illhoi Yoo, Brian Pierce, Amarda Shehu, Vijay K. Gombar, Brian Chen, Vinay Pai, Jun Huan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages458-463
Number of pages6
ISBN (Electronic)9781467367981
DOIs
StatePublished - Dec 16 2015
Externally publishedYes
EventIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States
Duration: Nov 9 2015Nov 12 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Other

OtherIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
CountryUnited States
CityWashington
Period11/9/1511/12/15

Keywords

  • KNN
  • SFLA
  • biomedical data
  • classification accuracy
  • feature selection

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Health Informatics
  • Biomedical Engineering

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  • Cite this

    Dai, Y., Hu, B., Su, Y., Mao, C., Chen, J., Zhang, X., Moore, P., Xu, L., & Cai, H. (2015). Feature selection of high-dimensional biomedical data using improved SFLA for disease diagnosis. In L. M. Schapranow, J. Zhou, X. T. Hu, B. Ma, S. Rajasekaran, S. Miyano, I. Yoo, B. Pierce, A. Shehu, V. K. Gombar, B. Chen, V. Pai, & J. Huan (Eds.), Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 (pp. 458-463). [7359728] (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2015.7359728