Cost-sensitive Bayesian network learning using sampling

Nashnush, EB; Vadera, S

Abstract

A significant advance in recent years has been the development of cost-sensitive decision tree learners, recognising that real world classification problems need to take account of costs of misclassification and not just focus on accuracy. The literature contains well over 50 cost-sensitive decision tree induction algorithms, each with varying performance profiles. Obtaining good Bayesian networks can be challenging and hence several algorithms have been proposed for learning their structure and parameters from data. However, most of these algorithms focus on learning Bayesian networks that aim to maximise the accuracy of classifications. Hence an obvious question that arises is whether it is possible to develop cost-sensitive Bayesian networks and whether they would perform better than cost-sensitive decision trees for minimising classification cost? This paper explores this question by developing a new Bayesian network learning algorithm based on changing the data distribution to reflect the costs of misclassification. The proposed method is explored by conducting experiments on over 20 data sets. The results show that this approach produces good results in comparison to more complex cost-sensitive decision tree algorithms.

Citation

Nashnush, E., & Vadera, S. (2014). Cost-sensitive Bayesian network learning using sampling. In Recent Advances on Soft Computing and Data Mining (467-476). Springer. https://doi.org/10.1007/978-3-319-07692-8_44

• Publication Date: Jan 1, 2014
• Deposit Date: May 29, 2015
• Publicly Available Date: Aug 13, 2018
• Publisher: Springer
• Pages: 467-476
• Series Title: Advances in Intelligent Systems and Computing
• Book Title: Recent Advances on Soft Computing and Data Mining
• ISBN: 9783319076911
• DOI: https://doi.org/10.1007/978-3-319-07692-8_44
• Keywords: Data mining, cost sensitive learning, Bayesian networks
• Publisher URL: http://dx.doi.org/10.1007/978-3-319-07692-8_44