A new genetic feature selection with neural network ensemble

Heesung Lee, Sungjun Hong, Euntai Kim

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


A neural network ensemble is a learning paradigm in which a finite collection of neural networks is trained for the same task. Ensembles generally show better classification and generalization performance than a single neural network does. In this paper, a new feature selection method for a neural network ensemble is proposed for pattern classification. The proposed method selects an adequate feature subset for each constituent neural network of the ensemble using a genetic algorithm. Unlike the conventional feature selection method, each neural network is only allowed to have some (not all) of the considered features. The proposed method can therefore be applied to huge-scale feature classification problems. Experiments are performed with four databases to illustrate the performance of the proposed method.

Original languageEnglish
Pages (from-to)1105-1117
Number of pages13
JournalInternational Journal of Computer Mathematics
Issue number7
Publication statusPublished - 2009 Jul

Bibliographical note

Funding Information:
This work was supported by grant number R01-2006-110-16-0 from Basic Research Program of the Korea Science and Engineering Foundation.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics


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