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A survey of cost-sensitive decision tree induction algorithms

Lomax, S and Vadera, S 2013, 'A survey of cost-sensitive decision tree induction algorithms' , ACM Computing Surveys, 45 (2) , 16:1-16:35.

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    Abstract

    The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field.

    Item Type: Article
    Uncontrolled Keywords: Data mining, cost-sensitive, decision trees
    Themes: Media, Digital Technology and the Creative Economy
    Subjects outside of the University Themes
    Schools: Colleges and Schools > College of Science & Technology
    Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Journal or Publication Title: ACM Computing Surveys
    Publisher: Association for Computing Machinery
    Refereed: Yes
    ISSN: 0360-0300
    Related URLs:
    Funders: Non funded research
    Depositing User: S Vadera
    Date Deposited: 09 Nov 2011 12:14
    Last Modified: 18 Aug 2014 15:09
    URI: http://usir.salford.ac.uk/id/eprint/18927

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