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.
|PDF - Accepted Version |
Download (638kB) | Preview
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.
|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|
|Funders:||Non funded research|
|Depositing User:||S Vadera|
|Date Deposited:||09 Nov 2011 12:14|
|Last Modified:||10 Apr 2013 18:46|
Document DownloadsMore statistics for this item...
Actions (login required)
|Edit record (repository staff only)|