Efficient huge-scale feature selection with speciated genetic algorithm

Jin Hyuk Hong, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

77 Citations (Scopus)


With increasing interest in bioinformatics, sophisticated tools are required to efficiently analyze gene information. The classification of gene expression profiles is crucial in those fields. Since the features of data obtained by microarray technology come to be over thousands, it is essential to extract useful information by selecting proper features. The information without any feature selection might be redundant so that this can deteriorate the performance of classification. The conventional feature selection method with genetic algorithm has difficulty for huge-scale feature selection. In this paper, we modify the representation of chromosome to be suitable for huge-scale feature selection and adopt speciation to enhance the performance of feature selection by obtaining diverse solutions. Experimental results with DNA microarray data from cancer patients show that the selected genes by the proposed method are useful for cancer classification.

Original languageEnglish
Pages (from-to)143-150
Number of pages8
JournalPattern Recognition Letters
Issue number2
Publication statusPublished - 2006 Jan 15

Bibliographical note

Funding Information:
The research was supported by Biometrics Engineering Research Center sponsored by Korean Ministry of Science and Technology, and by Brain Science and Engineering Research Program sponsored by Korean Ministry of Commerce, Industry and Energy.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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