On output independence and complementariness in rank-based multiple classifier decision systems

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Saranli A., Demirekler M.

PATTERN RECOGNITION, cilt.34, sa.12, ss.2319-2330, 2001 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 12
  • Basım Tarihi: 2001
  • Doi Numarası: 10.1016/s0031-3203(00)00175-8
  • Dergi Adı: PATTERN RECOGNITION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2319-2330
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

This study presents a theoretical analysis of output independence and complementariness between classifiers in a rank-based multiple classifier decision system in the context of the partitioned observation space theory. To enable such an analysis, an information theoretic interpretation of a rank-based multiple classifier system is developed and basic concepts from information theory are applied to develop measures for output independence and complementariness. It is shown that output independence of classifiers is not a requirement for achieving complementariness between these classifiers. Namely, output independence does not imply a performance improvement by combining multiple classifiers. A condition called dominance is shown to be important instead. The information theoretic measures proposed for output independence and complementariness are justified by simulated examples. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.