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Irrelevant feature and rule removal for structural associative classification

In the classification task, the presence of irrelevant features can significantly degrade the performance of classification algorithms,in terms of additional processing time, more complex models and the likelihood that the models have poor generalization power due to the over fitting problem.Practi...

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Main Authors: Mohd Shaharanee, Izwan Nizal, Jamil, Jastini
Format: Article
Language:English
Published: Universiti Utara Malaysia 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://repo.uum.edu.my/14313/1/95-110.pdf
http://repo.uum.edu.my/14313/
http://jict.uum.edu.my
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spelling my.uum.repo.143132016-04-27T07:08:12Z http://repo.uum.edu.my/14313/ Irrelevant feature and rule removal for structural associative classification Mohd Shaharanee, Izwan Nizal Jamil, Jastini QA75 Electronic computers. Computer science In the classification task, the presence of irrelevant features can significantly degrade the performance of classification algorithms,in terms of additional processing time, more complex models and the likelihood that the models have poor generalization power due to the over fitting problem.Practical applications of association rule mining often suffer from overwhelming number of rules that are generated, many of which are not interesting or not useful for the application in question.Removing rules comprised of irrelevant features can significantly improve the overall performance.In this paper, we explore and compare the use of a feature selection measure to filter out unnecessary and irrelevant features/attributes prior to association rules generation.The experiments are performed using a number of real-world datasets that represent diverse characteristics of data items.Empirical results confirm that by utilizing feature subset selection prior to association rule generation, a large number of rules with irrelevant features can be eliminated.More importantly, the results reveal that removing rules that hold irrelevant features improve the accuracy rate and capability to retain the rule coverage rate of structural associative association. Universiti Utara Malaysia 2015 Article PeerReviewed application/pdf en http://repo.uum.edu.my/14313/1/95-110.pdf Mohd Shaharanee, Izwan Nizal and Jamil, Jastini (2015) Irrelevant feature and rule removal for structural associative classification. Journal of ICT, 14. pp. 95-110. ISSN 1675-414X http://jict.uum.edu.my
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mohd Shaharanee, Izwan Nizal
Jamil, Jastini
Irrelevant feature and rule removal for structural associative classification
description In the classification task, the presence of irrelevant features can significantly degrade the performance of classification algorithms,in terms of additional processing time, more complex models and the likelihood that the models have poor generalization power due to the over fitting problem.Practical applications of association rule mining often suffer from overwhelming number of rules that are generated, many of which are not interesting or not useful for the application in question.Removing rules comprised of irrelevant features can significantly improve the overall performance.In this paper, we explore and compare the use of a feature selection measure to filter out unnecessary and irrelevant features/attributes prior to association rules generation.The experiments are performed using a number of real-world datasets that represent diverse characteristics of data items.Empirical results confirm that by utilizing feature subset selection prior to association rule generation, a large number of rules with irrelevant features can be eliminated.More importantly, the results reveal that removing rules that hold irrelevant features improve the accuracy rate and capability to retain the rule coverage rate of structural associative association.
format Article
author Mohd Shaharanee, Izwan Nizal
Jamil, Jastini
author_facet Mohd Shaharanee, Izwan Nizal
Jamil, Jastini
author_sort Mohd Shaharanee, Izwan Nizal
title Irrelevant feature and rule removal for structural associative classification
title_short Irrelevant feature and rule removal for structural associative classification
title_full Irrelevant feature and rule removal for structural associative classification
title_fullStr Irrelevant feature and rule removal for structural associative classification
title_full_unstemmed Irrelevant feature and rule removal for structural associative classification
title_sort irrelevant feature and rule removal for structural associative classification
publisher Universiti Utara Malaysia
publishDate 2015
url http://repo.uum.edu.my/14313/1/95-110.pdf
http://repo.uum.edu.my/14313/
http://jict.uum.edu.my
_version_ 1644281416584790016
score 13.211869

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