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Robust Combining of Disparate Classifiers Through Order StatisticsIntegrating 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 modeling 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 the median, the maximum and in general, the ith order statistic, are used. Furthermore, we analyze 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 domain data sets corroborate these findings.
Document ID
20030107362
Acquisition Source
Ames Research Center
Document Type
Preprint (Draft being sent to journal)
Authors
Tumer, Kagan (NASA Ames Research Center Moffett Field, CA, United States)
Ghosh, Joydeep (Texas Univ. Austin, TX, United States)
Date Acquired
September 7, 2013
Publication Date
November 1, 2001
Subject Category
Statistics And Probability
Funding Number(s)
CONTRACT_GRANT: NSF ECS-99-00353
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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