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Cost-sensitive Bayesian network learning using sampling

Nashnush, E and Vadera, S 2014, 'Cost-sensitive Bayesian network learning using sampling' , in: Recent Advances on Soft Computing and Data Mining , Advances in Intelligent Systems and Computing, 287 , Springer, pp. 467-476.

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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.

Item Type: Book Section
Uncontrolled Keywords: Data mining, cost sensitive learning, Bayesian networks
Themes: Media, Digital Technology and the Creative Economy
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Publisher: Springer
Refereed: Yes
Series Name: Advances in Intelligent Systems and Computing
ISBN: 9783319076911
Funders: Non funded research
Depositing User: S Vadera
Date Deposited: 29 May 2015 17:46
Last Modified: 29 Oct 2015 00:10
URI: http://usir.salford.ac.uk/id/eprint/34599

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