Chinese Journal of Computers   Full Text
Title	A Comparative Study of Cost-Sensitive Classifiers
Authors	LING Charles X. SHENG Victor S.
Address	(Department of Computer Science, The University of Western Ontario, London, Ontario N6A 5B7, Canada)
Year	2007
Issue	No.8(1203—1212)
Abstract &   Background	Abstract The authors briefly review the theory of cost-sensitive learning, and the existing cost-sensitive learning algorithms. The authors categorize cost-sensitive learning algorithms into direct cost-sensitive learning and cost-sensitive meta-learning, which converts cost-insensitive classifiers into cost-sensitive ones. The authors also propose a simple yet general and effective meta-learning method called Empirical Threshold Adjusting (ETA for short). The authors evaluate the performance of various cost-sensitive meta-learning algorithms including ETA. ETA almost always produces the lowest misclassification cost, and is least sensitive to the misclassification cost ratio. Other useful conclusions on cost-sensitive meta-learning methods are drawn. This is an improved and expanded version of the paper "Thresholding for Making Classifiers Cost-sensitive" by Victor S.Sheng and Charles X.Ling, published in AAAI 2006. keywords cost-sensitive learning; meta-learning; Empirical Threshold Adjusting(ETA)