Learning Lazy Rules to Improve the Performance of Classiiers

Based on an earlier study on lazy Bayesian rule learning, this paper introduces a general lazy learning framework, called LazyRule, that begins to learn a rule only when classifying a test case. The objective of the framework is to improve the performance of a base learning algorithm. It has the potential to be used for diierent types of base learning algorithms. LazyRule performs attribute elimination and training case selection using cross-validation to generate the most appropriate rule for each test case. At the consequent of the rule, it applies the base learning algorithm on the selected training subset and the remaining attributes to construct a classiier to make a prediction. This combined action seeks to build a better performing classiier for each test case than the classiier trained using all attributes and all training cases. We show empirically that LazyRule improves the performances of naive Bayesian classiiers and majority vote.