EBNO : evolution of cost-sensitive Bayesian networks

Nashnush, EB; Vadera, S

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, while 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: (i) an algorithm that induces cost-insensitive Bayesian networks to provide a base
line, (ii) ICET, a well-known algorithm that uses Genetic Algorithms to induce cost-sensitive decision trees, (iii) use of
MetaCost to induce cost-sensitive Bayesian networks via bagging (iv) use of AdaBoost to induce cost-sensitive Bayesian
networks and (v) 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 to the algorithms that produce single
interpretable models and performs just as well as algorithms that use bagging and boosting methods.

Citation

Nashnush, E., & Vadera, S. (2020). EBNO : evolution of cost-sensitive Bayesian networks. Expert Systems, 37(3), e12495. https://doi.org/10.1111/exsy.12495

• Journal Article Type: Article
• Acceptance Date: Oct 25, 2019
• Online Publication Date: Dec 17, 2019
• Publication Date: Jun 16, 2020
• Deposit Date: Nov 8, 2019
• Publicly Available Date: Dec 17, 2020
• Journal: Expert Systems
• Print ISSN: 0266-4720
• Electronic ISSN: 1468-0394
• Publisher: Wiley
• Volume: 37
• Issue: 3
• Pages: e12495
• DOI: https://doi.org/10.1111/exsy.12495
• Publisher URL: https://doi.org/10.1111/exsy.12495
• Related Public URLs: https://onlinelibrary.wiley.com/journal/14680394
• Additional Information: Access Information : This is the peer reviewed version of the following article: Nashnush, E, Vadera, S. EBNO: Evolution of cost‐sensitive Bayesian networks. Expert Systems. 2020; 37:e12495., which has been published in final form at https://doi.org/10.1111/exsy.12495. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.