A novel discretizer for knowledge discovery approaches based on rough sets

Knowledge discovery approaches based on rough sets have successful application in machine learning and data mining. As these approaches are good at dealing with discrete values, a discretizer is required when the approaches are applied to continuous attributes. In this paper, a novel adaptive discretizer based on a statistical distribution index is proposed to preprocess continuous valued attributes in an instance information system, so that the knowledge discovery approaches based on rough sets can reach a high decision accuracy. The experimental results on benchmark data sets show that the proposed discretizer is able to improve the decision accuracy.