JACIII Vol.19 No.6 pp. 747-758
doi: 10.20965/jaciii.2015.p0747


Improving Rough Set Rule-Based Classification by Supplementary Rules

Masahiro Inuiguchi and Keisuke Washimi

Graduate School of Engineering Science, Osaka University
Toyonaka, Osaka 560-8531, Japan

May 18, 2015
July 28, 2015
November 20, 2015
rough set, MLEM2, robustness measure, supplementary rules
In rough set approaches, decision rules are induced from a given data set consisting of attribute values and a decision value. Induced rules are used to classify new objects, but this classification is not perfect, perhaps because the given data set does not include all possible patterns. No induced decision rules are matched totally for objects having missing patterns, and partially matched decision rules are used to estimate their classes. The classification accuracy of such an object is usually lower than that of an object totally matching decision rules. To improve the classification accuracy, we propose adding supplementary rules to the induced rules, defining the supplementary rules to improve the classification accuracy of objects only partially matching decision rules. We propose an algorithm for inducing supplementary rules, considering four classifiers consisting of supplementary rules together with originally induced rules.We investigate their performance. We also compare their classification accuracies to that of conventional classifier with originally induced rules.
Cite this article as:
M. Inuiguchi and K. Washimi, “Improving Rough Set Rule-Based Classification by Supplementary Rules,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.6, pp. 747-758, 2015.
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