Feature Selection for Optimized High-Dimensional Biomedical Data Using an Improved Shuffled Frog Leaping Algorithm

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

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which influence the predictive accuracy of diagnosis. Without a feature selection algorithm, it is difficult for the existing classification techniques to accurately identify patterns in the features. The purpose of feature selection is to not only identify a feature subset from an original set of features [without reducing the predictive accuracy of classification algorithm] but also reduce the computation overhead in data mining. In this paper, we present our improved shuffled frog leaping algorithm which introduces a chaos memory weight factor, an absolute balance group strategy, and an adaptive transfer factor. Our proposed approach 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. To evaluate the effectiveness of our proposed method, we have employed the K-nearest neighbor method with a comparative analysis in which we compare our proposed approach with genetic algorithms, particle swarm optimization, and the shuffled frog leaping algorithm. Experimental results show that our improved algorithm achieves improvements in the identification of relevant subsets and in classification accuracy.

Original languageEnglish (US)
Article number7551172
Pages (from-to)1765-1773
Number of pages9
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume15
Issue number6
DOIs
StatePublished - Nov 1 2018

Keywords

  • Shuffled frog leaping algorithm
  • biomedical data
  • classification accuracy
  • feature selection
  • k-nearest neighbor

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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