Fast Rare Category Detection Using Nearest Centroid Neighborhood

摘要：

　　Rare category detection is an open challenge in data mining.The existing approaches to this problem often have some flaws,such as inappropriate investigation scopes,high time complexity,and limited applicable conditions,which will degrade their performance and reduce their usability.In this paper,we present FRANC an effective and efficient solution for rare category detection.It adopts an investigation scope based on k-nearest centroid neighbors with an automatically selected k,which helps the algorithm capture the real changes on local densities and data distribution caused by the presence of rare categories.By using our proposed pruning method,the identification of k-nearest centroid neighbors,which is the most computationally expensive step in FRANC,will be much faster for each data example.Extensive experimental results on real data sets demonstrate the effectiveness and efficiency of FRANC.

作者: Song Wang Hao Huang Yunjun Gao Tieyun Qian Liang Hong Zhiyong Peng

作者单位: State Key Laboratory of Software Engineering,Wuhan University,Wuhan,China College of Computer Science,Zhejiang University,Hangzhou,China School of Information Management,Wuhan University,Wuhan,China

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 383-394

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）