'Misclassification error' greedy heuristic to construct decision trees for inconsistent decision tables

Mohammad Azad, Mikhail Moshkov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

A greedy algorithm has been presented in this paper to construct decision trees for three different approaches (many-valued decision, most common decision, and generalized decision) in order to handle the inconsistency of multiple decisions in a decision table. In this algorithm, a greedy heuristic ‘misclassification error’ is used which performs faster, and for some cost function, results are better than ‘number of boundary subtables’ heuristic in literature. Therefore, it can be used in the case of larger data sets and does not require huge amount of memory. Experimental results of depth, average depth and number of nodes of decision trees constructed by this algorithm are compared in the framework of each of the three approaches.
Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Knowledge Discovery and Information Retrieval
PublisherSciTePress
ISBN (Print)9789897580482
DOIs
StatePublished - 2014

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