CSNL: A cost-sensitive non-linear decision tree algorithm

Vadera, S

Abstract

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification.

The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes.


The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are
compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date.
The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster.
The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes.

Citation

Vadera, S. (2010). CSNL: A cost-sensitive non-linear decision tree algorithm. ACM transactions on knowledge discovery from data, 4(2), 1-25. https://doi.org/10.1145/1754428.1754429

• Journal Article Type: Article
• Publication Date: May 1, 2010
• Deposit Date: Jun 24, 2010
• Publicly Available Date: Apr 5, 2016
• Journal: ACM Transactions on Knowledge Discovery from Data (TKDD)
• Print ISSN: 1556-4681
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Issue: 2
• Pages: 1-25
• DOI: https://doi.org/10.1145/1754428.1754429
• Keywords: decision tree learning, cost-sensitive learning, machine learning, AI
• Publisher URL: http://dx.doi.org/10.1145/1754428.1754429