Features selection and rule removal for frequent association rule based classification
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, selecti...
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| Main Authors: | , |
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| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2013
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| 主題: | |
| 在線閱讀: | http://repo.uum.edu.my/12044/1/PID65.pdf http://repo.uum.edu.my/12044/ http://www.icoci.cms.net.my/proceedings/2013/TOC.html |
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| 總結: | 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. |
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