Classification Approach based on Rough Mereology

Citation:
Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Classification Approach based on Rough Mereology", In Proceedings of the Second International Symposium on Intelligent Informatics (ISI'13), , Mysore, India, 23-24 August, 20, 2013.

Date Presented:

23-24 August, 20

This article presents a classification approach based on granular computing
combined with rough set. The proposed classification approach used the theory
of rough mereology and fuzzification in order to classify input datasets into sets
of optimized granules. The proposed approach was applied to five datasets of the
UC Irvine Machine Learning Repository. The Abalone dataset that consists of 4177
objects and eight attributes was selected as an illustrative example. Empirically obtained
experimental results demonstrated that the proposed rough mereology based
classification approach obtained better performance compared to other experienced
proposed classification approaches.

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