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Features selection and rule removal for frequent association rule based classification


Mohd Shaharanee, Izwan Nizal and Jamil, Jastini (2013) Features selection and rule removal for frequent association rule based classification. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28 -30 August 2013, Kuching, Sarawak, Malaysia.

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Abstract

The performance of association rule based classification is notably deteriorated with the existence of irrelevant and redundant features and complex attributes.Association rules naturally often suffer from a large volume of rules generated, many of which are not interesting and useful.Thus, selecting relevant feature and/or removing unrelated rules can significantly improve the association rule performance.In this paper, we explored and compared feature selection measures to filter out irrelevant and redundant features prior to association rules generation.Rules that encompassed with irrelevant/redundant features were removed. Based on the experimental results, removing rules that hold irrelevant features slightly improve the accuracy rate and capable to retain the rule coverage rate.

Item Type: Conference or Workshop Item (Paper)
Additional Information: features selection, rules removal, frequent item set mining
Uncontrolled Keywords: features selection, rules removal, frequent item set mining
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: College of Arts and Sciences
Depositing User: Mr. Izwan Nizal Mohd Shaharanee
Date Deposited: 25 Aug 2014 08:04
Last Modified: 25 Aug 2014 08:04
URI: http://repo.uum.edu.my/id/eprint/12044

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