Title :
Deleting or keeping outliers for classifier training?
Author :
Tallon-Ballesteros, Antonio J. ; Riquelme, Jose C.
Author_Institution :
Dept. of Languages & Comput. Syst., Univ. of Seville, Seville, Spain
fDate :
July 30 2014-Aug. 1 2014
Abstract :
This paper introduces two statistical outlier detection approaches by classes. Experiments on binary and multi-class classification problems reveal that the partial removal of outliers improves significantly one or two performance measures for C4.5 and 1-nearest neighbour classifiers. Also, a taxonomy of problems according to the amount of outliers is proposed.
Keywords :
data mining; statistical analysis; binary classification problems; classifier training; data mining; multiclass classification problems; nearest neighbour classifiers; statistical outlier detection; Detectors; Training; attribute noise; classification; inter-quartile range; outlier detection; partial removal; statistical outlier detection;
Conference_Titel :
Nature and Biologically Inspired Computing (NaBIC), 2014 Sixth World Congress on
Conference_Location :
Porto
Print_ISBN :
978-1-4799-5936-5
DOI :
10.1109/NaBIC.2014.6921892
