Combining Classifiers Using Correspondence Analysis

Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)

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Christopher Merz


Several effective methods for improving the performance of a sin(cid:173) gle learning algorithm have been developed recently. The general approach is to create a set of learned models by repeatedly apply(cid:173) ing the algorithm to different versions of the training data, and then combine the learned models' predictions according to a pre(cid:173) scribed voting scheme. Little work has been done in combining the predictions of a collection of models generated by many learning algorithms having different representation and/or search strategies. This paper describes a method which uses the strategies of stack(cid:173) ing and correspondence analysis to model the relationship between the learning examples and the way in which they are classified 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 consistently performs as well or better than other combining techniques on a suite of data sets.