Using Balanced Random Forests on Load Spectrum Data for Classifying Component Failures of a Hybrid Electric Vehicle Fleet

: 2014

URI: http://libsearch.um.ac.ir:80/fum/handle/fum/1101450

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Using Balanced Random Forests on Load Spectrum Data for Classifying Component Failures of a Hybrid Electric Vehicle Fleet

contributor author	Bergmeir, Philipp
contributor author	Nitsche, Christof
contributor author	Nonnast, Jurgen
contributor author	Bargende, Michael
contributor author	Antony, Peter
contributor author	Keller, Uwe
date accessioned	2020-03-12T22:59:08Z
date available	2020-03-12T22:59:08Z
date issued	2014
identifier other	7033148.pdf
identifier uri	http://libsearch.um.ac.ir:80/fum/handle/fum/1101450?locale-attribute=en
format	general
language	English
publisher	IEEE
title	Using Balanced Random Forests on Load Spectrum Data for Classifying Component Failures of a Hybrid Electric Vehicle Fleet
type	Conference Paper
contenttype	Metadata Only
identifier padid	8243791
subject keywords	Authentication
subject keywords	n Biometrics (access control)
subject keywords	n Control systems
subject keywords	n Databases
subject keywords	n Educational institutions
subject keywords	n Fingerprint recognition
subject keywords	n Vehicles
subject keywords	n authentication
subject keywords	n biometric system
subject keywords	n fingerprint
subject keywords	n motorists
subject keywords	n token-based
identifier doi	10.1109/I4CT.2014.6914180
journal title	achine Learning and Applications (ICMLA), 2014 13th International Conference on
filesize	323168
citations	0

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