Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Practical approximation of optimal multivariate discretization
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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We present a new discretization method in the contextof supervised learning. This method entitled HyperClusterFinder is characterized by its supervised and polytheticbehavior. The method is based on the notion of clustersand processes in two steps. First, a neighborhood graphconstruction from the learning database allows discoveringhomogenous clusters. Second, the minimal and maximalvalues of each cluster are transferred to each dimension inorder to define some boundaries to cut the continuous attributein a set of intervals. The discretization abilities ofthis method are illustrated by some examples, in particular,processing the XOR problem.