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On combining classifiers using sum and product rules
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DOI：10.1016/S0167-8655(01)00073-3.png)
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This paper presents a comparative study of the performance of arithmetic and geometric means as rules to combine multiple classifiers. For problems with two classes., we prove that these combination rules are equivalent when using two classifiers and the sum of the estimates of the a posteriori probabilities is equal to one. We also prove that the case of a two class problem and a combination of two classifiers is the only one where such equivalence occurs. We present experiments illustrating the equivalence of the rules under the above mentioned assumptions. (C) 2001 Elsevier Science B.V. All rights reserved.
Keyword:
classification
combining classifiers
classifier fusion
k nearest-neighbours
neural networks
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