Clustering-based undersampling in class-imbalanced data

Wei Chao Lin, Chih Fong Tsai, Ya Han Hu, Jing Shang Jhang

Research output: Contribution to journalArticlepeer-review

330 Scopus citations

Abstract

Class imbalance is often a problem in various real-world data sets, where one class (i.e. the minority class) contains a small number of data points and the other (i.e. the majority class) contains a large number of data points. It is notably difficult to develop an effective model using current data mining and machine learning algorithms without considering data preprocessing to balance the imbalanced data sets. Random undersampling and oversampling have been used in numerous studies to ensure that the different classes contain the same number of data points. A classifier ensemble (i.e. a structure containing several classifiers) can be trained on several different balanced data sets for later classification purposes. In this paper, we introduce two undersampling strategies in which a clustering technique is used during the data preprocessing step. Specifically, the number of clusters in the majority class is set to be equal to the number of data points in the minority class. The first strategy uses the cluster centers to represent the majority class, whereas the second strategy uses the nearest neighbors of the cluster centers. A further study was conducted to examine the effect on performance of the addition or deletion of 5 to 10 cluster centers in the majority class. The experimental results obtained using 44 small-scale and 2 large-scale data sets revealed that the clustering-based undersampling approach with the second strategy outperformed five state-of-the-art approaches. Specifically, this approach combined with a single multilayer perceptron classifier and C4.5 decision tree classifier ensembles delivered optimal performance over both small- and large-scale data sets.

Original languageEnglish
Pages (from-to)17-26
Number of pages10
JournalInformation Sciences
Volume409-410
DOIs
StatePublished - 1 Oct 2017

Keywords

  • Class imbalance
  • Classifier ensembles
  • Clustering
  • Imbalanced data
  • Machine learning

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