Boosting Adaptive Weighted Broad Learning System for Multi-Label LearningOACSTPCDEI
Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning
Multi-label classification is a challenging problem that has attracted significant attention from researchers,particu-larly in the domain of image and text attribute annotation.How-ever,multi-label datasets are prone to serious intra-class and inter-class imbalance problems,which can significantly degrade the classification performance.To address the above issues,we propose the multi-label weighted broad learning system(MLW-BLS)from the perspective of label imbalance weighting and label correlation mining.Further,we propose the multi-label adaptive weighted broad learning system(MLAW-BLS)to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set.Extensive experi-ments are conducted on various datasets to evaluate the effective-ness of the proposed model,and the results demonstrate its supe-riority over other advanced approaches.
Yuanxin Lin;Zhiwen Yu;Kaixiang Yang;Ziwei Fan;C.L.Philip Chen
School of Computer Science and Engineering in South China University of Technology,Guangzhou 510650,ChinaSchool of Computer Science and Engineering in South China University of Technology,Guangzhou 510650,China||Pengcheng Laboratory,Shenzhen 518066,China
Broad learning systemlabel correlation mininglabel imbalance weightingmulti-label imbalance
《自动化学报（英文版）》 2024 (011)
2204-2219 / 16
This work was supported in part by the National Key R&D Program of China(2023YFA1011601),the Major Key Project of PCL,China(PCL2023AS7-1),in part by the National Natural Science Foundation of China(U21A20478,62106224,92267203),in part by the Science and Technology Major Project of Guangzhou(202007030006),in part by the Major Key Project of PCL(PCL2021A09),and in part by the Guangzhou Science and Technology Plan Project(2024A04J3749).
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