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Soft Computing
Erschienen in: Soft Computing 11/2015

01.11.2015 | Methodologies and Application

NEATER: filtering of over-sampled data using non-cooperative game theory

verfasst von: B. A. Almogahed, I. A. Kakadiaris

Erschienen in: Soft Computing | Ausgabe 11/2015

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Abstract

In this paper, we present a method for the filteriNg of ovEr-sampled dAta using non-cooperaTive gamE theoRy (NEATER) to address the imbalanced data problem. Specifically, the problem is formulated as a non-cooperative game where all the data are players and the goal is to uniformly and consistently label all of the synthetic data created by any over-sampling technique. The proposed algorithm does not require any prior assumptions and selects representative synthetic instances while generating a very small number of noisy data. We present extensive experimental results over a large collection of datasets using three different classifiers to demonstrate the advantages of our method.
Vorheriger Artikel Ostrowski type inequalities and applications in numerical integration for interval-valued functions
Nächster Artikel Option pricing for an uncertain stock model with jumps

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Metadaten
Titel
NEATER: filtering of over-sampled data using non-cooperative game theory
verfasst von
B. A. Almogahed
I. A. Kakadiaris
Publikationsdatum
01.11.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 11/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-014-1484-5

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