Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2007

Abstract

Cost-sensitive decision tree learning is very important and popular in machine learning and data mining community. There are many literatures focusing on misclassification cost and test cost at present. In real world application, however, the issue of time-sensitive should be considered in cost-sensitive learning. In this paper, we regard the cost of time-sensitive in cost-sensitive learning as waiting cost (referred to WC), a novelty splitting criterion is proposed for constructing cost-time sensitive (denoted as CTS) decision tree for maximal decrease the intangible cost. And then, a hybrid test strategy that combines the sequential test with the batch test strategies is adopted in CTS learning. Finally, extensive experiments show that our algorithm outperforms the other ones with respect to decrease in misclassification cost.

Keywords

Decision Tree, Test Strategy, Test Cost, Intangible Cost, Misclassification Cost

Discipline

Computer Engineering | Databases and Information Systems

Publication

Proceedings of the 2nd International Conference on Knowledge Science, Engineering Management Location, Melbourne, Australia, 2007 November 28-30

First Page

447

Last Page

436

Identifier

10.1007/978-3-540-76719-0_44

Publisher

Springer

City or Country

Melbourne, Australia

Additional URL

https://doi.org/10.1007/978-3-540-76719-0_44

Download

Share

COinS