Robust Combining of Disparate Classifiers through Order Statistics

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Robust Combining of Disparate Classifiers through Order Statistics
: Integrating the outputs of multiple classifiers via combiners or meta-learners has led to substantial improvements in several difficult pattern recognition problems. In this article, we investigate a family of combiners based on order statistics, for robust handling of situations where there are large discrepancies in performance of individual classifiers. Based on a mathematical modelling of how the decision boundaries are affected by order statistic combiners, we derive expressions for the reductions in error expected when simple output combination methods based on the median, the maximum and in general, the ith order statistic, are used. Furthermore, we analyse the trim and spread combiners, both based on linear combinations of the ordered classifier outputs, and show that in the presence of uneven classifier performance, they often provide substantial gains over both linear and simple order statistics combiners. Experimental results on both real world data and standard public dom...
Kagan Tumer, Joydeep Ghosh
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 1999
Where CORR
Authors Kagan Tumer, Joydeep Ghosh
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