Under-sampling class imbalanced datasets by combining clustering analysis and instance selection

Chih Fong Tsai, Wei Chao Lin, Ya Han Hu, Guan Ting Yao

Research output: Contribution to journalArticlepeer-review

107 Scopus citations


Class-imbalanced datasets, i.e., those with the number of data samples in one class being much larger than that in another class, occur in many real-world problems. Using these datasets, it is very difficult to construct effective classifiers based on the current classification algorithms, especially for distinguishing small or minority classes from the majority class. To solve the class imbalance problem, the under/oversampling techniques have been widely used to reduce and enlarge the numbers of data samples in the majority and minority classes, respectively. Moreover, the combinations of certain sampling approaches with ensemble classifiers have shown reasonably good performance. In this paper, a novel undersampling approach called cluster-based instance selection (CBIS) that combines clustering analysis and instance selection is introduced. The clustering analysis component groups similar data samples of the majority class dataset into ‘subclasses’ while the instance selection component filters out unrepresentative data samples from each of the ‘subclasses’. The experimental results based on the KEEL dataset repository show that the CBIS approach can make bagging and boosting-based MLP ensemble classifiers perform significantly better than six state-of-the-art approaches, regardless of what kinds of clustering (affinity propagation and k-means) and instance selection (IB3, DROP3 and GA) algorithms are used.

Original languageEnglish
Pages (from-to)47-54
Number of pages8
JournalInformation Sciences
StatePublished - Mar 2019


  • Class imbalance
  • Clustering
  • Data mining
  • Ensemble classifiers
  • Instance selection


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