NDLI: A hybrid Machine Learning methodology for imbalanced datasets

Content Provider	IEEE Xplore Digital Library
Author	Lipitakis, A.-D. Kotsiantis, S.
Copyright Year	2014
Description	Author affiliation: Dept. of Math., Univ. of Patras, Patra, Greece (Lipitakis, A.-D.; Kotsiantis, S.)
Abstract	In the Machine Learning systems several imbalanced data sets exhibit skewed class distributions in which most cases are allocated to a class and far fewer cases to a smaller one. A classifier induced from an imbalanced data set has usually a low error rate for the majority class and an unacceptable error rate for the minority class. In this paper a synoptic review of the various related methodologies is given, a new ensemble methodology is introduced and an experimental study with other ensembles is presented. The proposed method that combines the power of OverBagging and Rotation Forest algorithms improves the identification of a difficult small class, while keeping the classification ability of the other class in an acceptable accuracy level.
Starting Page	252
Ending Page	257
File Size	174958
Page Count	6
File Format	PDF
e-ISBN	9781479961719
DOI	10.1109/IISA.2014.6878762
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-07
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Learning systems Accuracy Supervised machine learning Ensembles of classifiers Imbalanced data sets Classification algorithms Decision trees Bagging Computational intelligence Principal component analysis
Content Type	Text
Resource Type	Article

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