NDLI: New Oversampling Approaches Based on Polynomial Fitting for Imbalanced Data Sets

Content Provider	IEEE Xplore Digital Library
Author	Gazzah, S. Ben Amara, N.E.
Copyright Year	2008
Description	Author affiliation: Nat. Eng. Sch., Sousse (Gazzah, S.; Ben Amara, N.E.)
Abstract	In classification tasks, class-modular strategy has been widely used. It has outperformed classical strategy for pattern classification task in many applications. However, in some modular architecture, such as one against all in support vector machines classifier, the training dataset for one class risks to heavily outnumber the other classes. In this challenging situation, the trained classifier will accurately classify the majority class; nevertheless, it marginalizes the minority class. As a result, True Negatives rate (TNr) will be very high while the True Positives rate (TPr) will be low. The main goal of this work is to improve TPr without much sacrifice in TNr. In this paper, we propose oversampling the minority class using polynomial fitting functions. Four new approaches were proposed: star topology, bus topology, polynomial curve topology and mesh topology. Star and mesh topologies approach had led to the best performances.
Starting Page	677
Ending Page	684
File Size	1202469
Page Count	8
File Format	PDF
ISBN	9780769533377
DOI	10.1109/DAS.2008.74
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-16
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation majority class Text analysis Data engineering Topology Convergence Support vector machines class-modular strategy imbalanced data sets Support vector machine classification Pattern classification Training data polynomial fitting functions Polynomials writer identification system minority class
Content Type	Text
Resource Type	Article

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