A hybrid feature selection algorithm for gene expression data classification

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

    Research output: Contribution to journalArticle

    90 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
    Publication statusAccepted/In press - 2016 May 14


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

    ASJC Scopus subject areas

    • Computer Science Applications
    • Cognitive Neuroscience
    • Artificial Intelligence

    Fingerprint Dive into the research topics of 'A hybrid feature selection algorithm for gene expression data classification'. Together they form a unique fingerprint.

  • Cite this