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

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2001-12-01

Author

Saranlı, Afşar

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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.

Subject Keywords

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

URI

https://hdl.handle.net/11511/38438

Journal

PATTERN RECOGNITION

DOI

https://doi.org/10.1016/s0031-3203(00)00175-8

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

A. Saranlı, “On output independence and complementariness in rank-based multiple classifier decision systems,” PATTERN RECOGNITION, pp. 2319–2330, 2001, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38438.