Bayesian network-based over-sampling method (BOSME) with application to indirect cost-sensitive learning
Delgado de la Torre, Rosario (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Nuñez Gonzalez, Jose David (Universitat Autònoma de Barcelona. Departament de Matemàtiques)

Fecha:	2022
Resumen:	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.
Ayudas:	Agencia Estatal de Investigación PGC2018-097848-B-I0
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Engineering ; Mathematics and computing
Publicado en:	Scientific reports, Vol. 12 (May 2022) , art. 8724, ISSN 2045-2322

DOI: 10.1038/s41598-022-12682-8
PMID: 35610323

18 p, 1.9 MB

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