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Majority Re-sampling via Sub-class Clustering for Imbalanced Datasets by Shih-Wen Ke; Chih-Fong Tsai; Yi-Ying Pan; Wei-Chao Lin is a Computer Science article available to read on EtoBox.

What is Majority Re-sampling via Sub-class Clustering for Imbalanced Datasets about?

Many real world domain problem datasets are class imbalanced where the number of data in a given class is much less than in the other classes. In related literatures, under-and over-sampling techniques are widely used techniques to re-balance the class imbalanced datasets. However, their limitations include the risk of removing representative majority class data samples and the overfitting problem because of generating a large number of synthetic minority class data samples. Therefore, a novel approach, namely Majority Re-sampling visa Sub-class Clustering (MRSC) is introduced. It uses a clustering algorithm to group the majority class data into several clusters, i.e. sub-classes. Then, a new training set containing multiple sub-classes and a minority class is produced, after which the classifier is trained using this new multi-class dataset which has a lower imbalance ratio than the original dataset. The experimental results obtained using 44 two-class imbalanced datasets show that MRSC combined with the k-NN classifiers, including single and ensemble classifiers, significantly outperforms the other classifiers as well as seven state-of-the-art re-sampling approaches. Moreover, fo

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Author

Shih-Wen Ke; Chih-Fong Tsai; Yi-Ying Pan; Wei-Chao Lin

Publisher

Informa UK Limited

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)