NDLI: Classification-Error Cost Minimization Strategy: DCMS

Content Provider	IEEE Xplore Digital Library
Author	Parikh, Devi Chen, Tsuhan
Copyright Year	2007
Description	Author affiliation: Carnegie Mellon University, Department of Electrical and Computer Engineering, Pittsburgh, PA, USA. dparikh@cmu.edu (Parikh, Devi) \|\| Carnegie Mellon University, Department of Electrical and Computer Engineering, Pittsburgh, PA, USA. tsuhan@cmu.edu (Chen, Tsuhan)
Abstract	Several classification applications such as intrusion detection, biometric recognition, etc. have different costs associated with different classification errors. In such scenarios, the goal is to minimize the cost incurred, and not the classification error rate itself. This paper proposes a Cost Minimization Strategy, dCMS, which when applied to classifiers, provides a boost in the performance by reducing the cost incurred due to classification errors. dCMS is classifier-type independent, however it exploits the statistical properties of the trained classifier. It does not require classifiers to be retrained, which is particularly advantageous in scenarios where the costs vary dynamically. Convincing results are provided which indicate the statistically significant reduction in cost incurred by applying dCMS, in a diverse set of classification scenarios with datasets and classifiers of varying complexities.
Starting Page	620
Ending Page	624
File Size	258279
Page Count	5
File Format	PDF
ISBN	9781424411979
DOI	10.1109/SSP.2007.4301333
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cost function Error analysis Intrusion detection Biometrics Fingerprint recognition Histograms Distributed computing Application software Computer errors Volatile organic compounds combining classifiers cost minimization dCMS intrusion detection volatile organic compounds optical character recognition Learn++
Content Type	Text
Resource Type	Article

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