Advances in Class Noise Detection (CROSBI ID 566924)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Sluban, Borut ; Gamberger, Dragan ; Lavrač, Nada
engleski
Advances in Class Noise Detection
Noise filtering is usually used in data preprocessing to improve the accuracy of induced classifiers. Our goal is different: we aim at detecting noisy instances to be inspected by the domain expert in the phase of data understanding. Consequently, our noise detection algorithms should have high precision of class noise detection, where the precision-recall trade-off is modeled using the F-measure. New variants of class noise detection algorithms have been developed, including the high agreement random forest filter which ensures very high precision of identified erroneous data instances.
machine learning; noise detection; random forest
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Podaci o prilogu
1105-1106.
2010.
objavljeno
Podaci o matičnoj publikaciji
Coelho, Helder ; Studer, Rudi ; Wooldridge, Michael
978-1-60750-605-8
Podaci o skupu
19th European Conference on Artificiel Intelligence, ECAI 2010
predavanje
16.08.2010-20.08.2010
Lisabon, Portugal