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Abstract: Let us consider a set of training examples described by continuous or symbolic attributes with categorical classes. In this paper we present a measure of the potential quality of a region of the attribute space to be represented as a rule condition to classify unseen cases. The aim is to take into account the distribution of the classes of the examples. The resulting measure, called impurity level, is inspired by a similar measure used in the instance-based algorithm IB3 for selecting suitable... (Update)
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BibTeX entry: (Update)
@misc{ ranilla-heuristic,
author = "Jose Ranilla and Oscar Luaces and Antonio Bahamonde",
title = "A Heuristic for Learning Decision Trees and Pruning Them into Classification
Rules",
url = "citeseer.ist.psu.edu/731660.html" }
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[Article contains additional citations not shown here]
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