FUSINTER: a method for discretization of continuous attributes
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Machine Learning
A Modified Chi2 Algorithm for Discretization
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
Knowledge Discovery in Multiple Databases
Knowledge Discovery in Multiple Databases
Multiknowledge for decision making
Knowledge and Information Systems
Multi-knowledge extraction and application
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Hi-index | 0.00 |
When symbolic AI approaches are applied to handle continuous valued attributes, there is a requirement to transform the continuous attribute values to symbolic data. In this paper, a novel distribution-index-based discretizer is proposed for such a transformation. Based on definitions of dichotomic entropy and a compound distributional index, a simple criterion is applied to discretize continuous attributes adaptively. The dichotomic entropy indicates the homogeneity degree of the decision value distribution, and is applied to determine the best splitting point. The compound distributional index combines both the homogeneity degrees of attribute value distributions and the decision value distribution, and is applied to determine which interval should be split further; thus, a potentially improved solution of the discretization problem can be found efficiently. Based on multiple reducts in rough set theory, a multiknowledge approach can attain high decision accuracy for information systems with a large number of attributes and missing values. In this paper, our discretizer is combined with the multiknowledge approach to further improve decision accuracy for information systems with continuous attributes. Experimental results on benchmark data sets show that the new discretizer can improve not only the multiknowledge approach, but also the naïve Bayes classifier and the C5.0 tree.