A hybrid feature selection algorithm for gene expression data classification

Huijuan Lu, Junying Chen, Ke Yan*, Qun Jin, Yu Xue, Zhigang Gao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

227 Citations (Scopus)


In the DNA microarray research field, the increasing sample size and feature dimension of the gene expression data prompt the development of an efficient and robust feature selection algorithm for gene expression data classification. In this study, we propose a hybrid feature selection algorithm that combines the mutual information maximization (MIM) and the adaptive genetic algorithm (AGA). Experimental results show that the proposing MIMAGA-Selection method significantly reduces the dimension of gene expression data and removes the redundancies for classification. The reduced gene expression dataset provides highest classification accuracy compared to conventional feature selection algorithms. We also apply four different classifiers to the reduced dataset to demonstrate the robustness of the proposed MIMAGA-Selection algorithm.

Original languageEnglish
Pages (from-to)56-62
Number of pages7
Publication statusPublished - 2017 Sept 20


  • Adaptive genetic algorithm
  • Feature selection
  • Gene expression data
  • Mutual information maximization

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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