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SIGIR
2002
ACM

Probabilistic combination of text classifiers using reliability indicators: models and results

9 years 2 months ago
Probabilistic combination of text classifiers using reliability indicators: models and results
The intuition that different text classifiers behave in qualitatively different ways has long motivated attempts to build a better metaclassifier via some combination of classifiers. We introduce a probabilistic method for combining classifiers that considers the contextsensitive reliabilities of contributing classifiers. The method harnesses reliability indicators--variables that provide a valuable signal about the performance of classifiers in different situations. We provide background, present procedures for building metaclassifiers that take into consideration both reliability indicators and classifier outputs, and review a set of comparative studies undertaken to evaluate the methodology. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval; I.2.6 [Artificial Intelligence]: Learning; I.5.1 [Pattern Recognition]: Models General Terms Algorithms, Experimentation. Keywords Text classification, classifier combination, metacla...
Paul N. Bennett, Susan T. Dumais, Eric Horvitz
Added 23 Dec 2010
Updated 23 Dec 2010
Type Journal
Year 2002
Where SIGIR
Authors Paul N. Bennett, Susan T. Dumais, Eric Horvitz
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