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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2015 - 3


Automatic Mining of Numerical Classification Rules with Parliamentary Optimization Algorithm

KIZILOLUK, S. See more information about KIZILOLUK, S. on SCOPUS See more information about KIZILOLUK, S. on IEEExplore See more information about KIZILOLUK, S. on Web of Science, ALATAS, B. See more information about ALATAS, B. on SCOPUS See more information about ALATAS, B. on SCOPUS See more information about ALATAS, B. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,850 KB) | Citation | Downloads: 539 | Views: 2,282

Author keywords
classification algorithms, computational intelligence, data mining, heuristic algorithms, optimization

References keywords
optimization(15), algorithm(7), science(5), parliamentary(5), mining(5), classification(5), rules(4), global(4), alatas(4)
Blue keywords are present in both the references section and the paper title.

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

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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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Cited-By ISI Web of Science

Web of Science® Times Cited: 6 [View]
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SCOPUS® Times Cited: 7
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Cited-By CrossRef

[1] Web Pages Classification with Parliamentary Optimization Algorithm, Kiziloluk, Soner, Ozer, Ahmet Bedri, International Journal of Software Engineering and Knowledge Engineering, ISSN 0218-1940, Issue 03, Volume 27, 2017.
Digital Object Identifier: 10.1142/S0218194017500188

[2] Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus, Singh, Namrata, Singh, Pradeep, Biocybernetics and Biomedical Engineering, ISSN 0208-5216, Issue 1, Volume 40, 2020.
Digital Object Identifier: 10.1016/j.bbe.2019.10.001

[3] SM-RuleMiner: Spider monkey based rule miner using novel fitness function for diabetes classification, Cheruku, Ramalingaswamy, Edla, Damodar Reddy, Kuppili, Venkatanareshbabu, Computers in Biology and Medicine, ISSN 0010-4825, Issue , 2017.
Digital Object Identifier: 10.1016/j.compbiomed.2016.12.009

[4] A novel hybrid GA–PSO framework for mining quantitative association rules, Moslehi, Fateme, Haeri, Abdorrahman, Martínez-Álvarez, Francisco, Soft Computing, ISSN 1432-7643, Issue 6, Volume 24, 2020.
Digital Object Identifier: 10.1007/s00500-019-04226-6

[5] ANT_FDCSM: A novel fuzzy rule miner derived from ant colony meta-heuristic for diagnosis of diabetic patients, Anuradha, , Singh, Akansha, Gupta, Gaurav, Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, Issue 1, Volume 36, 2019.
Digital Object Identifier: 10.3233/JIFS-172240

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Faculty of Electrical Engineering and Computer Science
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