Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 39, Issue 2-3, Pages 235-255Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2004.10.001
Keywords
imprecise probabilities; uncertainty; upper entropy; imprecision; non-specificity; classification; classification trees; credal sets
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We present an application of the measure of entropy for credal sets: as a branching criterion for constructing classification trees based on imprecise probabilities which are determined with the imprecise Dirichlet model. We also justify the use of upper entropy as a global uncertainty measure for credal sets and present a deduction of this measure. We have carried out several experiments in which credal classification trees are built taking a global uncertainty measure as a basis. The results show how the introduced methodology improves the performance of traditional methods (Naive Bayes and C4.5), by providing a much lower error rate. (c) 2004 Elsevier Inc. All rights reserved.
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