AHP-Based Classifier Combination
نویسندگان
چکیده
Classifier combinations are effective techniques for difficult pattern recognition problems such as speech recognition where the combination of differently trained classifiers can produce a more robust phoneme classification on noisy datasets. In this paper we investigate traditional linear combination schemes (e.g. arithmetic mean and least squares methods), and propose a new combiner based on the Analytic Hierarchy Process (AHP), a method frequently applied in mathematical psychology and multi-criteria decision making. In addition, we experimentally compare the applicability of these linear combination schemes using neural network classifiers on a speech recognition framework and two test sets from the UCI repository.
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