|4/2015 - 3|
Automatic Mining of Numerical Classification Rules with Parliamentary Optimization AlgorithmKIZILOLUK, S. , ALATAS, B.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,850 KB) | Citation | Downloads: 494 | Views: 1,881|
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)
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
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.
Web of Science® Times Cited: 4 [View]
View record in Web of Science® [View]
View Related Records® [View]
SCOPUS® Times Cited: 6
View record in SCOPUS® [Free preview]
 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 [CrossRef]
 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 [CrossRef]
 A novel hybrid GA–PSO framework for mining quantitative association rules, Moslehi, Fateme, Haeri, Abdorrahman, Martínez-Álvarez, Francisco, Soft Computing, ISSN 1432-7643, 2019.
Digital Object Identifier: 10.1007/s00500-019-04226-6 [CrossRef]
 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 [CrossRef]
Disclaimer: All information displayed above was retrieved by using remote connections to respective databases. For the best user experience, we update all data by using background processes, and use caches in order to reduce the load on the servers we retrieve the information from. As we have no control on the availability of the database servers and sometimes the Internet connectivity may be affected, we do not guarantee the information is correct or complete. For the most accurate data, please always consult the database sites directly. Some external links require authentication or an institutional subscription.
Web of Science® is a registered trademark of Clarivate Analytics, Scopus® is a registered trademark of Elsevier B.V., other product names, company names, brand names, trademarks and logos are the property of their respective owners.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.