Bayesian network-based over-sampling method (BOSME) with application to indirect cost-sensitive learning

Rosario Delgado de la Torre, José-David Núñez-González

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Traditional supervised learning algorithms do not satisfactorily solve the classification problem on imbalanced data sets, since they tend to assign the majority class, to the detriment of the minority class classification. In this paper, we introduce the Bayesian network-based over-sampling method (BOSME), which is a new over-sampling methodology based on Bayesian networks. Over-sampling methods handle imbalanced data by generating synthetic minority instances, with the benefit that classifiers learned from a more balanced data set have a better ability to predict the minority class. What makes BOSME different is that it relies on a new approach, generating artificial instances of the minority class following the probability distribution of a Bayesian network that is learned from the original minority classes by likelihood maximization. We compare BOSME with the benchmark synthetic minority over-sampling technique (SMOTE) through a series of experiments in the context of indirect cost-sensitive learning , with some state-of-the-art classifiers and various data sets, showing statistical evidence in favor of BOSME, with respect to the expected (misclassification) cost.
Original languageEnglish
Article number8724
Pages (from-to)8724
Number of pages18
JournalScientific Reports
Volume12
Issue number1
Early online date24 May 2022
DOIs
Publication statusPublished - 24 May 2022

Keywords

  • Algorithms
  • Bayes Theorem

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