期刊
PATTERN RECOGNITION
卷 34, 期 2, 页码 299-314出版社
ELSEVIER SCI LTD
DOI: 10.1016/S0031-3203(99)00223-X
关键词
classifier fusion; combination of multiple classifiers; decision templates; fuzzy similarity; behavior-knowledge-space; fuzzy integral; Dempster-Shafer; class-conscious fusion; class-indifferent fusion
Multiple classifier fusion may generate more accurate classification than each of the constituent classifiers. Fusion is often based on fixed combination rules like the product and average. Only under strict probabilistic conditions can these rules be justified. We present here a simple rule for adapting the class combiner to the application. c decision templates (one per class) are estimated with the same training set that is used for the set of classifiers. These templates are then matched to the decision profile of new incoming objects by some similarity measure. We compare 11 versions of our model with 14 other techniques for classifier fusion on the Satimage and Phoneme datasets from the database ELENA. Our results show that decision templates based on integral type measures of similarity are superior to the other schemes on both data sets. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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