Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning  

作　　者：Yuanxin Lin Zhiwen Yu Kaixiang Yang Ziwei Fan C.L.Philip Chen 

机构地区：[1]the School of Computer Science and Engineering in South China University of Technology [2]IEEE [3]the Pengcheng Laboratory

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第11期2204-2219,共16页自动化学报（英文版）

基　　金：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);in part by the Guangzhou Science and Technology Plan Project (2024A04J3749)。

摘　　要：Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, 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 experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.

关 键 词：Broad learning system label correlation mining label imbalance weighting multi-label imbalance 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]