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<p>The Synthetic Minority Oversampling TEchnique (SMOTE) is known as the benchmark method to solve class imbalance learning. Since SMOTE was proposed, many variants of it have emerged, which are classified into two types: pre-processing and post-processing. However, most of the pre-processing methods do not filter the noisy samples; at the same time, the post-processing methods do not give attention to the focus area data. In this paper, we present an oversampling method based on kmeans-SMOTE and Iterative Partition Filter (KSIPF), which overcomes the shortcomings of the above methods. Firstly, KSIPF uses <i>k</i>-means to cluster the data and selects the clusters to oversample, and then, IPF is used to remove the noise samples from the data. Then, KSIPF is compared with the SMOTE and its variants on 30 synthetic imbalanced data sets and 20 real-world imbalanced data sets, and the balanced data sets are used to train SVM and AdaBoost classifiers to determine whether it is effective. Finally, the experiment results demonstrate that KSIPF performs better than the comparisons, including area under the curve, F1-measure, and the statistical test.</p>

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KSIPF: an effective noise filtering oversampling method based on k-means and iterative-partitioning filter

  • 期刊论文

  • 发表日期：

    2025年3月10日

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  • Pengfei Sun,
  • Zhiping Wang,
  • Liyan Jia,
  • Xiaoxi Wang

摘要

The Synthetic Minority Oversampling TEchnique (SMOTE) is known as the benchmark method to solve class imbalance learning. Since SMOTE was proposed, many variants of it have emerged, which are classified into two types: pre-processing and post-processing. However, most of the pre-processing methods do not filter the noisy samples; at the same time, the post-processing methods do not give attention to the focus area data. In this paper, we present an oversampling method based on kmeans-SMOTE and Iterative Partition Filter (KSIPF), which overcomes the shortcomings of the above methods. Firstly, KSIPF uses k-means to cluster the data and selects the clusters to oversample, and then, IPF is used to remove the noise samples from the data. Then, KSIPF is compared with the SMOTE and its variants on 30 synthetic imbalanced data sets and 20 real-world imbalanced data sets, and the balanced data sets are used to train SVM and AdaBoost classifiers to determine whether it is effective. Finally, the experiment results demonstrate that KSIPF performs better than the comparisons, including area under the curve, F1-measure, and the statistical test.