Clustering-based incremental learning for imbalanced data classification

Yuxin Liu, Guangyu Du, Chenke Yin, Hachao Zhang, Jia Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Imbalanced data classification presents a significant challenge when there is a substantial disparity in sample sizes across different classes. This issue severely affects classifier accuracy in predicting minority classes, hampering numerous real-world applications. Traditional methods address data imbalance by using undersampling or oversampling techniques. However, these methods may lead to information loss during sample reduction or introduce noise and model bias through synthetic sample generation. In this paper, we introduce DRIL, an innovative clustering-based incremental learning approach designed to overcome these limitations and improve the classification of minority class samples. Specifically, we employ a “two-step clustering” method to rebalance the dataset, partitioning it into similar and representative sub-dataset. Subsequently, incremental learning is applied to enable the classifier to gradually acquire knowledge about these sub-data, establishing a comprehensive understanding of all features present in the imbalanced dataset. Experimental results on twenty datasets demonstrate that our incremental learning-based algorithm outperforms baseline methods in correctly classifying minority classes while exhibiting improved precision and F1 score performance.

Original languageEnglish
Article number111612
JournalKnowledge-Based Systems
Publication statusPublished - 23 May 2024


  • Classification
  • Clustering
  • DIRL
  • Imbalance data
  • Incremental learning


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