Nashnush, E and Vadera, S 2016, 'Learning cost-sensitive Bayesian networks via direct and indirect methods' , Integrated Computer-Aided Engineering, 24 , pp. 17-26.
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Cost-Sensitive learning has become an increasingly important area that recognizes that real world classification problems need to take the costs of misclassification and accuracy into account. Much work has been done on cost-sensitive decision tree learning, but very little has been done on cost-sensitive Bayesian networks. Although there has been significant research on Bayesian networks there has been relatively little research on learning cost-sensitive Bayesian networks. Hence, this paper explores whether it is possible to develop algorithms that learn cost-sensitive Bayesian networks by taking (i) an indirect approach that changes the data distribution to reflect the costs of misclassification; and (ii) a direct approach that amends an existing accuracy based algorithm for learning Bayesian networks. An empirical comparison of the new approaches is carried out with cost-sensitive decision tree learning algorithms on 33 data sets, and the results show that the new algorithms perform better in terms of misclassification cost and maintaining accuracy.
|Schools:||Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)|
|Journal or Publication Title:||Integrated Computer-Aided Engineering|
|Funders:||University of Salford|
|Depositing User:||S Vadera|
|Date Deposited:||29 Mar 2016 14:11|
|Last Modified:||25 Jan 2017 16:02|
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