Previous research has shown that a technique called error-correcting output coding (ECOC) can dramatically improve the classification accuracy of supervised learning algorithms that learn to classify data points into one of k 2 classes. This paper presents an investigation of why the ECOC technique works, particularly when employed with decision-tree learning algorithms. It shows that the ECOC method--like any form of voting or committee--can reduce the variance of the learning algorithm. Furthermore--unlike methods that simply combine multiple runs of the same learning algorithm--ECOC can correct for errors caused by the bias of the learning algorithm. Experiments show that this bias correction ability relies on the non-local behavior of C4.5.
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