Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9651739 | International Journal of Approximate Reasoning | 2005 | 21 Pages |
Abstract
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.
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Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
JoaquÃn Abellán, SerafÃn Moral,