文献类型：期刊文献

英文题名：Gravitation balanced multiple kernel learning for imbalanced classification

作者：Yang, Mengping[1,2];Wang, Zhe[1,2];Li, Yanqiong[2];Zhou, Yangming[1,2];Li, Dongdong[2];Du, Wenli[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2022

卷号：34

期号：16

起止页码：13807

外文期刊名：NEURAL COMPUTING & APPLICATIONS

收录：;EI(收录号：20221511943447);WOS:【SCI-EXPANDED(收录号:WOS:000781163300001)】；

基金：This work is supported by Shanghai Science and Technology Program "Distributed and generative few-shot algorithm and theory research" under Grant No. 20511100600, Natural Science Foundation of China under Grant No. 62076094, Shanghai Science and Technology Program "Federated based cross-domain and cross-task incremental learning" under Grant No. 21511100800, Natural Science Foundations of China under Grant No. 61806078, and National Science Foundation of China for Distinguished Young Scholars under Grant No. 61725301.

语种：英文

外文关键词：Imbalanced classification problem; Multiple kernel learning; Gravitation balanced; Regularization term

摘要：This paper considers a category of classification problems where the samples of different classes are not represented equally. It arises in a variety of application areas and has been widely studied in pattern recognition. This paper focuses on enhancing the original data representation by combining the gravitation-based method with multiple empirical kernel approach, this paper proposes a sample level method known as the gravitational balanced multiple kernel learning (GBMKL) method. Our proposed GBMKL method integrates gravity strategy to generate the gravitation balanced midpoint samples (GBMS) located on the classification boundary; meanwhile, the classification boundary can be rectified by the nearest neighbors of the boundary (NNB) samples, which can improve the generalization performance. We further design two regularization terms corresponding to GBMS and NNB to avoid overfitting. In the training and testing process, the samples are mapped into multiple empirical kernel spaces to obtain more sufficient data representation. We conduct extensive computational experiments on 54 imbalanced datasets including both artificial and real-word datasets selected from knowledge extraction based on evolutionary learning datasets, the experimental results reveal interesting insights and show the advantages of the proposed GBMKL approach for dealing with the imbalanced classification problems. In addition, parameter analysis of two regularization terms confirms their positive impacts on the classification performance.