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Enhancing rough set theory attributes selection of KDD Cup 1999

Attribute selection (Feature Selection) is a significant technique for data preprocessing and dimensionality reduction. Rough set has been used for attribute selection with great success. The optimal solution of rough set attribute selection is a subset of attributes called a reduct. Rough set uses...

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Main Authors: Jebur, Hamid H., Maarof, Mohd. Aizaini, Zainal, Anazida
Format: Article
Published: Asian Research Publishing Network 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/55033/
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spelling my.utm.550332017-02-15T07:28:44Z http://eprints.utm.my/id/eprint/55033/ Enhancing rough set theory attributes selection of KDD Cup 1999 Jebur, Hamid H. Maarof, Mohd. Aizaini Zainal, Anazida QA75 Electronic computers. Computer science Attribute selection (Feature Selection) is a significant technique for data preprocessing and dimensionality reduction. Rough set has been used for attribute selection with great success. The optimal solution of rough set attribute selection is a subset of attributes called a reduct. Rough set uses approximation during reduction process to handle information inconsistency. However, some rough set approaches to attribute selection are inadequate at finding optimal reductions as no perfect heuristic can ensure optimality. Applying rough set for selecting the optimal subset of KDD Cup 1999 does not guarantee finding the optimal reduct of each class of this dataset due to the overlap between the lower and upper approximation of each class and the overlap between the reducts of all classes. This paper introduces a new approach to enhance the reduct of all classes by overcoming the overlap problem of rough set through adding union and voting attributes of all dataset classes as new reducts in addition to the normal reduct. The all reducts were evaluated by using different classification algorithms. The approach led to generate two generic attributes sets that achieved high and comparable accuracy rates as the normal attributes of rough set for the same dataset. Asian Research Publishing Network 2015-06 Article PeerReviewed Jebur, Hamid H. and Maarof, Mohd. Aizaini and Zainal, Anazida (2015) Enhancing rough set theory attributes selection of KDD Cup 1999. Journal of Theoretical and Applied Information Technology, 76 (3). pp. 393-400. ISSN 1992-8645
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Jebur, Hamid H.
Maarof, Mohd. Aizaini
Zainal, Anazida
Enhancing rough set theory attributes selection of KDD Cup 1999
description Attribute selection (Feature Selection) is a significant technique for data preprocessing and dimensionality reduction. Rough set has been used for attribute selection with great success. The optimal solution of rough set attribute selection is a subset of attributes called a reduct. Rough set uses approximation during reduction process to handle information inconsistency. However, some rough set approaches to attribute selection are inadequate at finding optimal reductions as no perfect heuristic can ensure optimality. Applying rough set for selecting the optimal subset of KDD Cup 1999 does not guarantee finding the optimal reduct of each class of this dataset due to the overlap between the lower and upper approximation of each class and the overlap between the reducts of all classes. This paper introduces a new approach to enhance the reduct of all classes by overcoming the overlap problem of rough set through adding union and voting attributes of all dataset classes as new reducts in addition to the normal reduct. The all reducts were evaluated by using different classification algorithms. The approach led to generate two generic attributes sets that achieved high and comparable accuracy rates as the normal attributes of rough set for the same dataset.
format Article
author Jebur, Hamid H.
Maarof, Mohd. Aizaini
Zainal, Anazida
author_facet Jebur, Hamid H.
Maarof, Mohd. Aizaini
Zainal, Anazida
author_sort Jebur, Hamid H.
title Enhancing rough set theory attributes selection of KDD Cup 1999
title_short Enhancing rough set theory attributes selection of KDD Cup 1999
title_full Enhancing rough set theory attributes selection of KDD Cup 1999
title_fullStr Enhancing rough set theory attributes selection of KDD Cup 1999
title_full_unstemmed Enhancing rough set theory attributes selection of KDD Cup 1999
title_sort enhancing rough set theory attributes selection of kdd cup 1999
publisher Asian Research Publishing Network
publishDate 2015
url http://eprints.utm.my/id/eprint/55033/
_version_ 1643653674045538304
score 13.211869

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