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

作　　者：Yi-Bo Wang Jun-Yi Hang Min-Ling Zhang 

机构地区：[1]School of Computer Science and Engineering,Southeast University,Nanjing 210096,China [2]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第7期1248-1261,共14页自动化学报（英文版）

基　　金：This work was supported by the National Science Foundation of China(62176055);the China University S&T Innovation Plan Guided by the Ministry of Education.

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 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]