期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 34, 期 5, 页码 2284-2297出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3106306
关键词
Learning systems; Optimization; Training; Feature extraction; Data mining; Boosting; Bagging; Adaptive subspace selection; class imbalance; ensemble learning; high-dimensional data; resampling
In this study, an adaptive subspace optimization ensemble method is proposed for high-dimensional imbalanced data classification. Multiple robust and discriminative subspaces are generated by adaptive subspace generation and rotated subspace optimization, and a resampling scheme is applied to construct class-balanced data. Experimental results demonstrate the superiority of this method over other imbalance learning approaches and classifier ensemble methods.
It is hard to construct an optimal classifier for high-dimensional imbalanced data, on which the performance of classifiers is seriously affected and becomes poor. Although many approaches, such as resampling, cost-sensitive, and ensemble learning methods, have been proposed to deal with the skewed data, they are constrained by high-dimensional data with noise and redundancy. In this study, we propose an adaptive subspace optimization ensemble method (ASOEM) for high-dimensional imbalanced data classification to overcome the above limitations. To construct accurate and diverse base classifiers, a novel adaptive subspace optimization (ASO) method based on adaptive subspace generation (ASG) process and rotated subspace optimization (RSO) process is designed to generate multiple robust and discriminative subspaces. Then a resampling scheme is applied on the optimized subspace to build a class-balanced data for each base classifier. To verify the effectiveness, our ASOEM is implemented based on different resampling strategies on 24 real-world high-dimensional imbalanced datasets. Experimental results demonstrate that our proposed methods outperform other mainstream imbalance learning approaches and classifier ensemble methods.
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