标题:Correlation-Based Weighted K-Labelsets for Multi-label Classification
作者:Xu, Jingyang; Ma, Jun
通讯作者:Ma, Jun
作者机构:[Xu, Jingyang; Ma, Jun] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China.
会议名称:18th Asia-Pacific Web Conference (APWeb)
会议日期:SEP 23-25, 2016
来源:Web Technologies and Applications, Pt I
出版年:2016
卷:9931
页码:408-419
DOI:10.1007/978-3-319-45814-4_33
关键词:Multi-label classification; Label correlation; Ensemble method;; k-labelsets
摘要:RAkEL(RAndom k-labELsets) is an effective ensemble multi-label classification method where each sub classifier is trained on a small randomly-selected subset of k labels, called k-labelset. However, random combination of labels may lead to the poor performance of sub classifiers and the method can not make full use of the label correlations. In this paper, we propose a novel ensemble multi-label classification method named LCWkEL(Label Correlations-based Weighted k-labELsets). Instead of randomly choosing subsets, we select a number of k-labelsets based on a label correlation matrix. Furthermore, considering the label correlations in different k-labelsets may have different influence on an instance, we construct a weight coefficient vector for an instance. Each dimension of the vector represents the weight coefficient for each sub classifier. For the multi-label classification of an unlabeled instance, LCWkEL calculates the weighted sum of all sub classifiers' predictions, which can improve the classification performance effectively. Experimental results on three areas of data sets show that the method proposed in this paper can obtain competitive performance compared with the RAkEL method and other high-performing multi-label classification methods.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989322095&doi=10.1007%2f978-3-319-45814-4_33&partnerID=40&md5=904a171536c7d1f1ca19195eca0bf161
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