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JCR Impact Factor: 0.800
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

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


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  4/2015 - 3

 HIGH-IMPACT PAPER 

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
 
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Download PDF pdficon (1,850 KB) | Citation | Downloads: 922 | Views: 3,357

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

Abstract
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Full text preview
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.


References | Cited By  «-- Click to see who has cited this paper

[1] J. Han and M. Kamber, Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann, San Francisco, ch. 1, 2006.

[2] P. Kosina, J. Gama, "Very fast decision rules for classification in data streams", Data Mining and Knowledge Discovery, vol. 29, no. 1, pp. 168-202, 2015.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 35]


[3] M. Hacibeyoglu, A. Arslan, S. Kahramanli, "A hybrid method for fast finding the reduct with the best classification accuracy", Advances in Electrical and Computer Engineering, vol. 13, no. 4, pp. 57-64, 2013.
[CrossRef] [Full Text] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]


[4] E. D. Ulker, A. Haydar, "Comparing the robustness of evolutionary algorithms on the basis of benchmark functions", Advances in Electrical and Computer Engineering, vol. 13, no. 2, pp. 59-64, 2013.
[CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 6]


[5] R. Ngamtawee, P. Wardkein, "Simplified genetic algorithm: simplify and improve RGA for parameter optimizations", Advances in Electrical and Computer Engineering, vol. 14, no. 4, pp. 55-64, 2014.
[CrossRef] [Full Text] [Web of Science Times Cited 3] [SCOPUS Times Cited 5]


[6] B. Alatas, "A novel chemistry based metaheuristic optimization method for mining of classification rules", Expert Systems with Applications, vol. 39, no. 12, pp. 11080-11088, 2012.
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 72]


[7] S. Bechikh, A. Chaabani, L.B. Said, "An efficient chemical reaction optimization algorithm for multiobjective optimization", IEEE Transactions on Cybernetics, vol. 45, no. 10, pp. 2051-2064, 2015.
[CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 78]


[8] M. Dorigo, V. Maniezzo, A. Colorni, "The ant system: Optimization by a colony of cooperating agents", IEEE Transactions on Systems, Man, and Cybernetics, Part B., vol. 26, no. 1, pp. 29-41, 2002.
[CrossRef] [Web of Science Times Cited 7268] [SCOPUS Times Cited 10034]


[9] B. Alatas, E. Akin, "FCACO: Fuzzy Classification Rules Mining Algorithm with Ant Colony Optimization", Lecture Notes in Computer Science, vol. 3612, pp. 787-797, 2005.
[CrossRef] [SCOPUS Times Cited 11]


[10] L. De Castro and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Springer, ch. 3, 2002.

[11] B. Alatas, E. Akin, "Mining Fuzzy Classification Rules Using an Artificial Immune System with Boosting", Lecture Notes in Computer Science, vol. 3631, pp. 283-293, 2005.
[CrossRef] [SCOPUS Times Cited 32]


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[CrossRef] [Web of Science Times Cited 27159] [SCOPUS Times Cited 33109]


[13] I. Birbil and S. Fang, "An electromagnetism-like mechanism for global optimization", Journal of Global Optimization, vol. 25, no. 3, pp. 263-282, 2003.
[CrossRef] [Web of Science Times Cited 522] [SCOPUS Times Cited 686]


[14] F. Glover, "Tabu search-part I", ORSA Journal on Computing, vol. 1, no. 3, pp. 190-206, 1989.
[CrossRef]


[15] E. Atashpaz-Gargari and C. Lucas, 2007, "Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition", IEEE Congress on Evolutionary Computation, 2007, pp. 4661-4667.
[CrossRef] [Web of Science Times Cited 1818] [SCOPUS Times Cited 2280]


[16] A. Borji, "A new global optimization algorithm inspired by parliamentary political competitions", Lecture Notes in Computer Science, vol. 4827, pp. 61-71, 2007.
[CrossRef] [SCOPUS Times Cited 35]


[17] R. V. Rao, V. J. Savsani, D. P. Vakharia, "Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems", Information Sciences, vol. 183, no. 1, pp. 1-15, 2012.
[CrossRef] [Web of Science Times Cited 1144] [SCOPUS Times Cited 1434]


[18] A. Borji and M. Hamidi, "A new approach to global optimization motivated by parliamentary political competitions", Int. Journal of Innovative Computing, Information and Control, vol. 5, no. 6, pp. 1643-1653, 2009.

[19] F. Altunbey, B. Alatas, "Overlapping community detection in social networks using parliamentary optimization algorithm", International Journal of Computer Networks and Applications, vol. 2, no. 1, 12-19, 2015.

[20] L. de-Marcos, A. Garcia-Cabot, E. Garcia-Lopez, J. Medina, "Parliamentary optimization to build personalized learning paths: Case study in web engineering curriculum", The International Journal of Engineering Education, vol. 31, no. 4, 2015.

[21] L. de-Marcos, A. GarcĂ­a, E. Garcia, J. J. Martinez, J. A. Gutierrez, R. Barchino, J. M. Gutierrez, J. R. Hilera, S. Oton, "An adaptation of the parliamentary metaheuristic for permutation constraint satisfaction", IEEE Congress on Evolutionary Computation (CEC), pp.1-8, 2010.
[CrossRef] [SCOPUS Times Cited 8]




References Weight

Web of Science® Citations for all references: 38,070 TCR
SCOPUS® Citations for all references: 47,830 TCR

Web of Science® Average Citations per reference: 1,730 ACR
SCOPUS® Average Citations per reference: 2,174 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-03-28 17:38 in 142 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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