Using Correspondence Analysis to Combine Classifiers

Authors:
Christopher J. Merz
Affiliations:
Department of Information and Computer Science, University of California, Irvine, CA 92697-3425. cmerz@uci.edu
Venue:
Machine Learning
Year:
1999

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Abstract

Several effective methods have been developed recently for improving predictive performance by generating and combining multiple learned models. The general approach is to create a set of learned models either by applying an algorithm repeatedly to different versions of the training data, or by applying different learning algorithms to the same data. The predictions of the models are then combined according to a voting scheme. This paper focuses on the task of combining the predictions of a set of learned models. The method described uses the strategies of stacking and Correspondence Analysis to model the relationship between the learning examples and their classification by a collection of learned models. A nearest neighbor method is then applied within the resulting representation to classify previously unseen examples. The new algorithm does not perform worse than, and frequently performs significantly better than other combining techniques on a suite of data sets.