中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble

文献类型:期刊论文

作者Yi-Bo Wang; Jun-Yi Hang; Min-Ling Zhang
刊名IEEE/CAA Journal of Automatica Sinica
出版日期2022
卷号9期号:7页码:1248-1261
关键词Clustering ensemble expectation-maximization al-gorithm label-specific features multi-label learning
ISSN号2329-9266
DOI10.1109/JAS.2022.105518
英文摘要Multi-label learning deals with objects associated with multiple class labels, and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance. Since each class might possess its own characteristics, the strategy of extracting label-specific features has been widely employed to improve the discrimination process in multi-label learning, where the predictive model is induced based on tailored features specific to each class label instead of the identical instance representations. As a representative approach, LIFT generates label-specific features by conducting clustering analysis. However, its performance may be degraded due to the inherent instability of the single clustering algorithm. To improve this, a novel multi-label learning approach named SENCE (stable label-Specific features gENeration for multi-label learning via mixture-based Clustering Ensemble) is proposed, which stabilizes the generation process of label-specific features via clustering ensemble techniques. Specifically, more stable clustering results are obtained by firstly augmenting the original instance repre-sentation with cluster assignments from base clusters and then fitting a mixture model via the expectation-maximization (EM) algorithm. Extensive experiments on eighteen benchmark data sets show that SENCE performs better than LIFT and other well-established multi-label learning algorithms.
源URL[http://ir.ia.ac.cn/handle/173211/48901]  
专题自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
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Yi-Bo Wang,Jun-Yi Hang,Min-Ling Zhang. Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(7):1248-1261.
APA Yi-Bo Wang,Jun-Yi Hang,&Min-Ling Zhang.(2022).Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble.IEEE/CAA Journal of Automatica Sinica,9(7),1248-1261.
MLA Yi-Bo Wang,et al."Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble".IEEE/CAA Journal of Automatica Sinica 9.7(2022):1248-1261.

入库方式: OAI收割

来源:自动化研究所

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