NDLI: Fuzzy ROC curves for unsupervised nonparametric ensemble techniques

Content Provider	IEEE Xplore Digital Library
Author	Evangelista, P.E. Embrechts, M.J. Bonissone, P. Szymanski, B.K.
Copyright Year	2005
Description	Author affiliation: Dept. of Decision Sci. & Eng. Syst., Rensselaer Polytech. Inst., Troy, NY, USA (Evangelista, P.E.; Embrechts, M.J.)
Abstract	This paper explores a novel ensemble technique for unsupervised classification using nonparametric statistics. Multiple classification systems (MCS), or ensemble techniques, involve considering several classification methods or multiple outputs from the same method and devising techniques to reach a decision. The performance of a binary classification system can be measured on a receiver operating characteristic (ROC) curve, and the area under the curve (AUC) is exactly the Wilcoxon rank sum or Mann-Whitney U statistic, both of which are nonparametric statistics based upon ranked data. Successful performance of an unsupervised ensemble can be measured through the AUC, and the performance of different aggregation techniques for the combination of the multiple classification system decision values, or rankings in this paper, is illustrated. Aggregation techniques are based upon fuzzy logic theory, creating the fuzzy ROC curve. The one-class SVM is utilized for the unsupervised classification.
Starting Page	3040
Ending Page	3045
File Size	1898885
Page Count	6
File Format	PDF
ISBN	0780390482
DOI	10.1109/IJCNN.2005.1556410
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-31
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Statistics Fuzzy logic Unsupervised learning Electronic mail Support vector machines Systems engineering and theory Computer science Area measurement Support vector machine classification Machine learning
Content Type	Text
Resource Type	Article

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