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	Local clustering Conformal Predictor for imbalanced data classification
	Wang, Huazhen ; Chen, Yewang ; Chen, Zhigang ; Yang, Fan ; Chen ZG(陈志刚) ; Yang F(杨帆)
	2013
关键词	Artificial intelligence Cluster analysis Taxonomies
英文摘要	Conference Name:9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2013. Conference Address: Paphos, Cyprus. Time:September 30, 2013 - October 2, 2013.; IFIP; Cyprus University of Technology; Frederick University, Cyprus; Royal Holloway, University of London; Cyprus Tourism Organization; The recently developed Conformal Predictor (CP) can provide calibrated confidence for prediction which is out of the traditional predictors' capacity. However, CP works for balanced data and fails in the case of imbalanced data. To handle this problem, Local Clustering Conformal Predictor (LCCP) which plugs a two-level partition into the framework of CP is proposed. In the first-level partition, the whole imbalanced training dataset is partitioned into some class-taxonomy data subsets. Secondly, the majority class examples proceed to be partitioned into some cluster-taxonomy data subsets by clustering method. To predict a new instance, LCCP selects the nearest cluster, incorporated with the minority class examples, to build a re-balanced training data. The designed LCCP model aims to not only provide valid confidence for prediction, but significantly improve the prediction efficiency as well. The experimental results show that LCCP model presents superiority than CP model for imbalanced data classification. ? IFIP International Federation for Information Processing 2013.
语种	英语
出处	http://dx.doi.org/10.1007/978-3-642-41142-7_43
出版者	Springer New York
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/85314]
专题	海洋环境－会议论文
推荐引用方式 GB/T 7714	Wang, Huazhen,Chen, Yewang,Chen, Zhigang,et al. Local clustering Conformal Predictor for imbalanced data classification. 2013-01-01.

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