Noise handling with extension matrices

HCV is a heuristic attribute-based induction algorithm based on the newly-developed extension matrix approach. By dividing the positive examples (PE) of a speciic class in a given example set into intersecting groups and adopting a set of strategies to nd a heuris-tic conjunctive formula in each group which covers all the group's positive examples and none of the negative examples (NE), it can nd a covering formula in the form of variable-valued logic for PE against NE in low-order polynomial time. The original algorithm performs quite well with those data sets where noise and continuous data are not of major concern. However, its performance decreases when the data sets are noisy and contain continuous attributes. This paper presents noise handling techniques developed and implemented in HCV (Version 2.0), a noise tolerant version of the HCV algorithm, and provides a performance comparison of HCV with other inductive algorithms C4.5 and NewID in noisy and continuous domains.