Probabilistic Combination of Text Classifiers Using Reliability Indicators: Models and Results - Bennett, Paul; Dumais, Susan; Horvitz, Eric (Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002) (An extended and revised version of this work appears in Information Retrieval, 2005.), 2002)
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 classifiersthat 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 metaclassi- fiersthat take into consideration both reliability indicators and classifier outputs, and review a set of comparative studies undertaken to evaluate the methodology