NDLI: Data Fusion for Outlier Detection through Pseudo-ROC Curves and Rank Distributions

Content Provider	IEEE Xplore Digital Library
Author	Evangelista, P.F. Embrechts, M.J. Szymanski, B.K.
Copyright Year	2006
Description	Author affiliation: United States Military Acad., West Point (Evangelista, P.F.)
Abstract	This paper proposes a novel method of fusing models for classification of unbalanced data. The unbalanced data contains a majority of healthy (negative) instances, and a minority of unhealthy (positive) instances. The applicability of this type of classification problem with security applications inspired the naming of such problems as security classification problems (SCP). The area under the ROC curve (AUC) is the metric utilized to measure classifier performance, and in order to better understand AUC and ROC behavior, pseudo-ROC curves created from simulated data are introduced. ROC curves depend entirely upon the rankings created by classifiers. The rank distributions discussed in this paper display classifier performance in a novel form, and the behavior of these rank distributions provides insight into classifier fusion for the SCP. Rank distributions, which illustrate the probability of a particular rank containing a positive or negative instance, will be introduced and used to explain why synergistic classifier fusion occurs.
Starting Page	2166
Ending Page	2173
File Size	253164
Page Count	8
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.246989
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electronic mail Data security Data engineering Systems engineering and theory Robustness Military computing Computer science Area measurement Modeling Displays
Content Type	Text
Resource Type	Article

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