Thresholding for making classifiers cost-sensitive

Victor S. Sheng, Charles X. Ling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

83 Scopus citations

Abstract

In this paper we propose a very simple, yet general and effective method to make any cost-insensitive classifiers (that can produce probability estimates) cost-sensitive. The method, called Thresholding, selects a proper threshold from training instances according to the misclassification cost. Similar to other cost-sensitive meta-learning methods, Thresholding can convert any existing (and future) costinsensitive learning algorithms and techniques into costsensitive ones. However, by comparing with the existing cost sensitive meta-learning methods and the direct use of the theoretical threshold, Thresholding almost always produces the lowest misclassification cost. Experiments also show that Thresholding has the least sensitivity on the misclassification cost ratio. Thus, it is recommended to use when the difference on misclassification costs is large.

Original languageEnglish
Title of host publicationProceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
Pages476-481
Number of pages6
StatePublished - 2006
Event21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06 - Boston, MA, United States
Duration: Jul 16 2006Jul 20 2006

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Conference

Conference21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
CountryUnited States
CityBoston, MA
Period07/16/0607/20/06

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  • Cite this

    Sheng, V. S., & Ling, C. X. (2006). Thresholding for making classifiers cost-sensitive. In Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06 (pp. 476-481). (Proceedings of the National Conference on Artificial Intelligence; Vol. 1).