An information-theoretic strategy for constructing multiple classifier systems

Hee Joong Kang, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Most studies on combining multiple classifiers have mainly focused on how to combine their classification results and only a few studies have investigated on how to construct multiple classifier systems from available classifiers pool. In this paper, an information-theoretic strategy based on information theory model is proposed for constructing the multiple classifier systems. Provided that the number of classifiers in the multiple classifier systems is restricted in advance, this proposed strategy is applied to the classifiers pool and examines the possible sets of classifiers with the information-theoretic measure, and then it selects some sets of classifiers as the multiple classifier system candidates. The multiple classifier system candidates were evaluated together with the other sets of classifiers in the recognition of unconstrained handwritten numerals. The experimental results supported that the proposed strategy was a promising approach.

Original languageEnglish
Pages (from-to)483-486
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Issue number2
Publication statusPublished - 2000

Bibliographical note

Funding Information:
'This research was supported by Creative Research Initiatives of the Ministry of Science and Technology, Korea.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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