On Fusing Multiple Instance Selection Results

C. F. Tsai, Y. H. Hu, M. C. Wang, K. E. Liu

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

Instance selection is an important preprocessing step in data mining. Its aim is to filter out unrepresentative data samples from a given training dataset, which allow the classifier to perform better than the one without instance selection. Since various instance selection algorithms have been proposed in the literature, no study considers applying the information fusion principle to combine multiple instance selection results. This paper uses three well-known instance selection algorithms, which are IB3, DROP3, and GA, and their selection results are combined via the union, intersection, and multi-intersection strategies. Our experimental results based on 50 various domains of datasets show that the union between GA and DROP3 performs the best. However, it does not produce the largest reduction rate. On the other hand, if both classification accuracy and reduction rate are considered, the union between DROP3 and IB3 is the better choice.

原文???core.languages.en_GB???
主出版物標題2019 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2019
發行者IEEE Computer Society
頁面1546-1549
頁數4
ISBN(電子)9781728138046
DOIs
出版狀態已出版 - 12月 2019
事件2019 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2019 - Macao, Macao
持續時間: 15 12月 201918 12月 2019

出版系列

名字IEEE International Conference on Industrial Engineering and Engineering Management
ISSN(列印)2157-3611
ISSN(電子)2157-362X

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???event.eventtypes.event.conference???2019 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2019
國家/地區Macao
城市Macao
期間15/12/1918/12/19

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