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PUBLISHER

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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  4/2013 - 10

 HIGHLY CITED PAPER 

A Hybrid Method for Fast Finding the Reduct with the Best Classification Accuracy

HACIBEYOGLU, M. See more information about HACIBEYOGLU, M. on SCOPUS See more information about HACIBEYOGLU, M. on IEEExplore See more information about HACIBEYOGLU, M. on Web of Science, ARSLAN, A. See more information about  ARSLAN, A. on SCOPUS See more information about  ARSLAN, A. on SCOPUS See more information about ARSLAN, A. on Web of Science, KAHRAMANLI, S. See more information about KAHRAMANLI, S. on SCOPUS See more information about KAHRAMANLI, S. on SCOPUS See more information about KAHRAMANLI, S. on Web of Science
 
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Download PDF pdficon (653 KB) | Citation | Downloads: 798 | Views: 3,497

Author keywords
artificial intelligence, classification algorithms, decision trees, discernibility function, feature selection

References keywords
rough(13), data(13), systems(11), knowledge(11), information(11), rule(10), learning(10), induction(10), approach(8), classification(7)
No common words between the references section and the paper title.

About this article
Date of Publication: 2013-11-30
Volume 13, Issue 4, Year 2013, On page(s): 57 - 64
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.04010
Web of Science Accession Number: 000331461300010
SCOPUS ID: 84890203115

Abstract
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Usually a dataset has a lot of reducts finding all of which is known to be an NP hard problem. On the other hand, different reducts of a dataset may provide different classification accuracies. Usually, for every dataset, there is only a reduct with the best classification accuracy to obtain this best one, firstly we obtain the group of attributes that are dominant for the given dataset by using the decision tree algorithm. Secondly we complete this group up to reducts by using discernibility function techniques. Finally, we select only one reduct with the best classification accuracy by using data mining classification algorithms. The experimental results for datasets indicate that the classification accuracy is improved by removing the irrelevant features and using the simplified attribute set which is derived from proposed method.


References | Cited By

Cited-By Clarivate Web of Science

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Cited-By CrossRef

[1] Prediction of users webpage access behaviour using association rule mining, GEETHARAMANI, R, REVATHY, P, JACOB, SHOMONA G, Sadhana, ISSN 0256-2499, Issue 8, Volume 40, 2015.
Digital Object Identifier: 10.1007/s12046-015-0424-0
[CrossRef]

[2] The logic transformations for reducing the complexity of the discernibility function-based attribute reduction problem, Hacibeyoglu, Mehmet, Salman, Mohammad Shukri, Selek, Murat, Kahramanli, Sirzat, Knowledge and Information Systems, ISSN 0219-1377, Issue 3, Volume 46, 2016.
Digital Object Identifier: 10.1007/s10115-015-0824-9
[CrossRef]

[3] Automatic Mining of Numerical Classification Rules with Parliamentary Optimization Algorithm, KIZILOLUK, S., ALATAS, B., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 15, 2015.
Digital Object Identifier: 10.4316/AECE.2015.04003
[CrossRef] [Full text]

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


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