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AbstractDecision 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.
CitationAzad M, Moshkov M (2016) Classification for Inconsistent Decision Tables. Lecture Notes in Computer Science: 525–534. Available: http://dx.doi.org/10.1007/978-3-319-47160-0_48.
Conference/Event nameInternational Joint Conference on Rough Sets, IJCRS 2016