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Stefan cel Mare
University of Suceava
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Computer Science
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ROMANIA

Print ISSN: 1582-7445
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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: 781 | Views: 3,452

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.


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References Weight

Web of Science® Citations for all references: 4,396 TCR
SCOPUS® Citations for all references: 21,913 TCR

Web of Science® Average Citations per reference: 92 ACR
SCOPUS® Average Citations per reference: 457 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 2024-04-19 12:04 in 149 seconds.




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