EBNO : evolution of cost-sensitive Bayesian networks

Nashnush, EB and Vadera, S ORCID: https://orcid.org/0000-0001-6041-2646 2020, 'EBNO : evolution of cost-sensitive Bayesian networks' , Expert Systems, 37 (3) , e12495.

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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.


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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Expert Systems
Publisher: Wiley
ISSN: 0266-4720
Related URLs:
Depositing User: S Vadera
Date Deposited: 08 Nov 2019 15:18
Last Modified: 16 Feb 2022 03:10
URI: http://usir.salford.ac.uk/id/eprint/52993

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