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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2015

01.12.2015

An Efficient Over-sampling Approach Based on Mean Square Error Back-propagation for Dealing with the Multi-class Imbalance Problem

verfasst von: R. Alejo, V. García, J. H. Pacheco-Sánchez

Erschienen in: Neural Processing Letters | Ausgabe 3/2015

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Abstract

In this paper a new dynamic over-sampling method is proposed, it is a hybrid method that combines a well known over-sampling technique (SMOTE) with the sequential back-propagation algorithm. The method is based on the back-propagation mean square error (MSE) for automatically identifying the over-sampling rate, i.e., it allows only the use of necessary training samples for dealing with the class imbalance problem and avoiding to increase excessively the (neural networks) NN training time. The main aim of the proposed method is to obtain a trade-off between NN classification performance and NN training time on scenarios where the training data set represents a multi-class classification problem, it is high imbalanced and it might request a large NN training time. Experimental results on fifteen multi-class imbalanced data sets show that the proposed method is promising.
Vorheriger Artikel A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification
Nächster Artikel Delay-Dependent Exponential Synchronization of Recurrent Neural Networks with Multiple Proportional Delays

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Fußnoten
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Metadaten
Titel
An Efficient Over-sampling Approach Based on Mean Square Error Back-propagation for Dealing with the Multi-class Imbalance Problem
verfasst von
R. Alejo
V. García
J. H. Pacheco-Sánchez
Publikationsdatum
01.12.2015
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2015
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-014-9376-3

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