EBNO: Evolution of cost‐sensitive Bayesian networks. Issue 3 (17th December 2019)

• Record Type: Journal Article
• Authors: Nashnush, Eman; Vadera, Sunil
• Other Names: Saen Reza Farzipoor guestEditor.; Song Malin guestEditor.; Fisher Ron guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-17
• Subjects: cost‐sensitive classification -- machine learning -- data mining; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12495
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: The last decade has seen an increase in the attention paid to the development of cost‐sensitive learning algorithms that aim to minimize misclassification costs while still maintaining accuracy. Most of this attention has been on cost‐sensitive decision tree learning, whereas relatively little attention has been paid to assess if it is possible to develop better cost‐sensitive classifiers based on Bayesian networks. Hence, this paper presents EBNO, an algorithm that utilizes Genetic algorithms to learn cost‐sensitive Bayesian networks, where genes are utilized to represent the links between the nodes in Bayesian networks and the expected cost is used as a fitness function. An empirical comparison of the new algorithm has been carried out with respect to (a) an algorithm that induces cost‐insensitive Bayesian networks to provide a base line, (b) ICET, a well‐known algorithm that uses Genetic algorithms to induce cost‐sensitive decision trees, (c) use of MetaCost to induce cost‐sensitive Bayesian networks via bagging (d) use of AdaBoost to induce cost‐sensitive Bayesian networks, and (e) use of XGBoost, a gradient boosting algorithm, to induce cost‐sensitive decision trees. An empirical evaluation on 28 data sets reveals that EBNO performs well in comparison with the algorithms that produce single interpretable models and performs just as well as algorithms that use bagging and boosting methods.