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CW-kNN: An efficient kNN-based model for imbalanced dataset classification
Xiang, Yi1; Cao, ZhongFeng2; Yao, ShaoWen1; He, Jing1
Department微波遥感部
Source PublicationACM International Conference Proceeding Series
2018
Pages7-11
DOI10.1145/3290420.3290431
Language英语
ISBN9781450365345
AbstractK nearest neighbor (kNN) method is a popular classification method in data mining because of its simple implementation and significant classification performance. However, kNN do not scale well to big datasets. In this paper, CLUKER, a novel kNN regression method based on hierarchical clustering, is proposed. CLUKER uses hierarchical clustering to divide the original dataset into several parts, effectively reducing the query scope of kNN. Moreover, in order to improve kNN's ability to handle imbalanced datasets, this paper proposes a novel weighting method based on local data distribution, called LD-Weighting method. In the end, having integrated the two algorithms, this paper proposes an efficient kNN-based model for imbalanced dataset classification called CW-kNN. The experimental results show that the proposed methods perform well on different datasets. © 2018 Association for Computing Machinery.
Conference Name4th International Conference on Communication and Information Processing, ICCIP 2018
Conference DateNovember 2, 2018 - November 4, 2018
Conference PlaceQingdao, China
Indexed ByEI
Citation statistics
Document Type会议论文
Identifierhttp://ir.nssc.ac.cn/handle/122/6965
Collection微波遥感部
Affiliation1.School of software, Yunnan University Kunming, China;
2.National Space Science Center, Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Xiang, Yi,Cao, ZhongFeng,Yao, ShaoWen,et al. CW-kNN: An efficient kNN-based model for imbalanced dataset classification[C],2018:7-11.
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