Comparing meta-learning algorithms
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
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In this paper we present a new meta-learning approach for Knowledge Integration. To generate accurate classifiers one can use combination techniques like Stacking, Bagging and Boosting. Such techniques are used for the generation of vote committees that produce decisions much more accurate than simple base classifiers. It is known that even using quite small partitions of the training database such techniques produce much more accurate decisions than a simple base classifier that uses all the training data. This is suitable for solving scalability problems. However, such techniques can not learn understandable knowledge, what is a drawback from the Knowledge Discover process point-of-view. To solve these problems, we introduce in this paper a Knowledge Integration technique capable of generate accurate and understandable rule sets taking as input base classifiers generated by a rule induction algorithm. Such rule sets are combined into a single rule set that, when evaluated over test instances, presents a better accuracy than any individual rule set and often outperforms Bagging and AdaBoosting.