A survey of cost-sensitive decision tree induction algorithms

Lomax, S; Vadera, S

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.

Citation

Lomax, S., & Vadera, S. (2013). A survey of cost-sensitive decision tree induction algorithms. ACM computing surveys, 45(2), 16:1-16:35. https://doi.org/10.1145/2431211.2431215

• Journal Article Type: Article
• Publication Date: Feb 1, 2013
• Deposit Date: Nov 9, 2011
• Publicly Available Date: Apr 5, 2016
• Journal: ACM Computing Surveys
• Print ISSN: 0360-0300
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 45
• Issue: 2
• Pages: 16:1-16:35
• DOI: https://doi.org/10.1145/2431211.2431215
• Keywords: Data mining, cost-sensitive, decision trees
• Publisher URL: http://dx.doi.org/10.1145/2431211.2431215
• Related Public URLs: http://csur.acm.org/