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KA-Ensemble: towards imbalanced image classification ensembling under-sampling and over-sampling

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Abstract

Imbalanced learning has become a research emphasis in recent years because of the growing number of class-imbalance classification problems in real applications. It is particularly challenging when the imbalanced rate is very high. Sampling, including under-sampling and over-sampling, is an intuitive and popular way in dealing with class-imbalance problems, which tries to regroup the original dataset and is also proved to be efficient. The main deficiency is that under-sampling methods usually ignore many majority class examples while over-sampling methods may easily cause over-fitting problem. In this paper, we propose a new algorithm dubbed KA-Ensemble ensembling under-sampling and over-sampling to overcome this issue. Our KA-Ensemble explores EasyEnsemble framework by under-sampling the majority class randomly and over-sampling the minority class via kernel based adaptive synthetic (Kernel-ADASYN) at meanwhile, yielding a group of balanced datasets to train corresponding classifiers separately, and the final result will be voted by all these trained classifiers. Through combining under-sampling and over-sampling in this way, KA-Ensemble is good at solving class-imbalance problems with large imbalanced rate. We evaluated our proposed method with state-of-the-art sampling methods on 9 image classification datasets with different imbalanced rates ranging from less than 2 to more than 15, and the experimental results show that our KA-Ensemble performs better in terms of accuracy (ACC), F-Measure, G-Mean, and area under curve (AUC). Moreover, it can be used in both dichotomy and multi-classification on both image classification and other class-imbalance problems.

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Authors and Affiliations

  1. Department of Electronic Engineering, College of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China

    Hao Ding, Zhaorui Gu, Zhibin Yu, Haiyong Zheng & Bing Zheng

  2. Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, 266003, China

    Bin Wei

  3. Department of Mathematics, School of Science and Engineering, University of Dundee, Dundee, DD1 4HN, UK

    Haiyong Zheng

  4. College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao, 266109, China

    Juan Li

Authors
  1. Hao Ding
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  2. Bin Wei
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  3. Zhaorui Gu
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  4. Zhibin Yu
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  5. Haiyong Zheng
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  6. Bing Zheng
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  7. Juan Li
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Correspondence to Bin Wei or Haiyong Zheng.

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This work was supported in part by the National Natural Science Foundation of China under Grants 61771440, 41776113 and 41674037, in part by the Qingdao Municipal Science and Technology Program under Grant 17-1-1-5-jch, in part by the China Scholarship Council under Grant 201806335022, and in part by the Foundation of Shandong provincial Key Laboratory of Digital Medicine and Computer Assisted Surgery under Grant SDKL-DMCAS-2018-07.

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Ding, H., Wei, B., Gu, Z. et al. KA-Ensemble: towards imbalanced image classification ensembling under-sampling and over-sampling. Multimed Tools Appl 79, 14871–14888 (2020). https://doi.org/10.1007/s11042-019-07856-y

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