Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble

doi:10.1109/JAS.2022.105518

CASIA OpenIR > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble
	Yi-Bo Wang; Jun-Yi Hang; Min-Ling Zhang
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2022
卷号	9 期号:7 页码:1248-1261
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.
关键词	Clustering ensemble expectation-maximization al-gorithm label-specific features multi-label learning
DOI	10.1109/JAS.2022.105518
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48901
专题	学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	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.

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