Classification and Optimization of Decision Trees for Inconsistent Decision Tables Represented as MVD Tables

Mohammad Azad, Mikhail Moshkov

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

4 Scopus citations

Abstract

Decision tree is a widely used technique to discover patterns from consistent data set. But if the data set is inconsistent, where there are groups of examples (objects) with equal values of conditional attributes but different decisions (values of the decision attribute), then to discover the essential patterns or knowledge from the data set is challenging. We consider three approaches (generalized, most common and many-valued decision) to handle such inconsistency. We created different greedy algorithms using various types of impurity and uncertainty measures to construct decision trees. We compared the three approaches based on the decision tree properties of the depth, average depth and number of nodes. Based on the result of the comparison, we choose to work with the many-valued decision approach. Now to determine which greedy algorithms are efficient, we compared them based on the optimization and classification results. It was found that some greedy algorithms Mult\_ws\_entSort, and Mult\_ws\_entML are good for both optimization and classification.
Original languageEnglish (US)
Title of host publicationProceedings of the 2015 Federated Conference on Computer Science and Information Systems
PublisherPolish Information Processing Society PTI
Pages31-38
Number of pages8
ISBN (Print)9788360810668
DOIs
StatePublished - Oct 11 2015

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