Classification for Inconsistent Decision Tables

Mohammad Azad, Mikhail Moshkov

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

2 Scopus citations

Abstract

Decision trees have been used widely to discover patterns from consistent data set. But if the data set is inconsistent, where there are groups of examples with equal values of conditional attributes but different labels, then to discover the essential patterns or knowledge from the data set is challenging. Three approaches (generalized, most common and many-valued decision) have been considered to handle such inconsistency. The decision tree model has been used to compare the classification results among three approaches. Many-valued decision approach outperforms other approaches, and M_ws_entM greedy algorithm gives faster and better prediction accuracy.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science
PublisherSpringer Nature
Pages525-534
Number of pages10
ISBN (Print)9783319471594
DOIs
StatePublished - Sep 29 2016

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