Oversampling framework based on sample subspace optimization with accelerated binary particle swarm optimization for imbalanced classification

被引:2
|
作者
Li, Junnan [1 ]
机构
[1] Chongqing Ind Polytech Coll, Sch Artificial Intelligence & Big Data, Chongqing 401120, Peoples R China
基金
中国国家自然科学基金;
关键词
Class-imbalanced classification; Oversampling methods; Instance selection; Sample subspace optimization; Particle swarm optimization; K-MEANS; SMOTE; DBSCAN;
D O I
10.1016/j.asoc.2024.111708
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In response to the need to generate synthetic minority class samples to extend minority classes, the SMOTE-based oversampling methods have been favored for class-imbalanced classification. They usually generate unnecessary noise when training data are not well separated. Although filtering-based oversampling methods are recognized as effective solutions for addressing noise generation through employing specific noise filters based on instance selection methods to remove suspicious noise, they suffer from the following issues: a) noise filters heavily rely on strong assumptions, causing low robustness to different datasets; b) noise filters are specially designed for a specific oversampling method, and are not easily extended to others; and c) noise filters have a relatively high time consumption. To address noise generation while overcoming the above issues a)-c), an oversampling framework based on sample subspace optimization with accelerated binary particle swarm optimization (OFSSO-ABPSO) is proposed. OF-SSO-ABPSO is a wrapping framework compatible with almost all the oversampling methods. First, in the framework, a SMOTE-based method is used to generate synthetic minority class samples. Second, a novel accelerated binary particle swarm optimization (ABPSO) algorithm with a new search space reduction strategy, a new particle update mechanism, and a new fitness function is proposed. Third, a novel ABPSO-based sample subspace optimization (SSO-ABPSO) method is proposed and used as a noise filter to remove suspicious noise from the training set and synthetic minority class samples. Experiments prove that, a) OF-SSO-ABPSO can improve 6 representative SMOTE variations by addressing noise generation, and b) OF-SSOABPSO outperforms 7 state-of-the-art filtering-based oversampling methods.
引用
收藏
页数:21
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