CW-kNN: An efficient kNN-based model for imbalanced dataset classification
文献类型：会议论文
| 作者 | Xiang, Yi1; Cao, ZhongFeng2; Yao, ShaoWen1; He, Jing1 |
| 出版日期 | 2018 |
| 会议日期 | November 2, 2018 - November 4, 2018 |
| 会议地点 | Qingdao, China |
| DOI | 10.1145/3290420.3290431 |
| 页码 | 7-11 |
| 英文摘要 | K 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. |
| 会议录 | ACM International Conference Proceeding Series
 |
| 语种 | 英语 |
| ISBN号 | 9781450365345 |
| 源URL | [http://ir.nssc.ac.cn/handle/122/6965]  |
| 专题 | 国家空间科学中心_微波遥感部 |
| 作者单位 | 1.School of software, Yunnan University Kunming, China; 2.National Space Science Center, Chinese Academy of Sciences, Beijing, China |
| 推荐引用方式 GB/T 7714 | Xiang, Yi,Cao, ZhongFeng,Yao, ShaoWen,et al. CW-kNN: An efficient kNN-based model for imbalanced dataset classification[C]. 见:. Qingdao, China. November 2, 2018 - November 4, 2018. |
入库方式： OAI收割
来源：国家空间科学中心
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