Publication: An Ensemble Associated Feature Subset Selection for Classification Problems
Issued Date
2016-01-14
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2-s2.0-84964873353
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Mahidol University
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SCOPUS
Bibliographic Citation
Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015. (2016), 63-67
Suggested Citation
Tanasanee Phienthrakul An Ensemble Associated Feature Subset Selection for Classification Problems. Proceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015. (2016), 63-67. doi:10.1109/ISCBI.2015.18 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/43519
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Title
An Ensemble Associated Feature Subset Selection for Classification Problems
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Abstract
© 2015 IEEE. Feature subset selection is an important problem in machine learning and data mining. If the suitable features are selected, the results of classification or prediction will be more accurate, while if the unsuitable features are used, the results may have no meaningful. This paper presents a method for feature subset selection that uses the ensemble technique to increase the efficiency of feature selection. Association rule mining is introduced to select the high relationship features. Bagging concept is applied to increase the confidence of selection. The experimental results show the efficiency of the proposed method that outperforms the efficiency of simple association feature subset selection.