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Automatic Mining of Numerical Classification Rules with Parliamentary Optimization AlgorithmKIZILOLUK, S. , ALATAS, B.
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classification algorithms, computational intelligence, data mining, heuristic algorithms, optimization
optimization(15), algorithm(7), science(5), parliamentary(5), mining(5), classification(5), rules(4), global(4), alatas(4)
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About this article
Date of Publication: 2015-11-30
Volume 15, Issue 4, Year 2015, On page(s): 17 - 24
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.04003
Web of Science Accession Number: 000368499800003
SCOPUS ID: 84949980538
In recent years, classification rules mining has been one of the most important data mining tasks. In this study, one of the newest social-based metaheuristic methods, Parliamentary Optimization Algorithm (POA), is firstly used for automatically mining of comprehensible and accurate classification rules within datasets which have numerical attributes. Four different numerical datasets have been selected from UCI data warehouse and classification rules of high quality have been obtained. Furthermore, the results obtained from designed POA have been compared with the results obtained from four different popular classification rules mining algorithms used in WEKA. Although POA is very new and no applications in complex data mining problems have been performed, the results seem promising. The used objective function is very flexible and many different objectives can easily be added to. The intervals of the numerical attributes in the rules have been automatically found without any a priori process, as done in other classification rules mining algorithms, which causes the modification of datasets.
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