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Open AccessArticle

A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem

1
Pattern Recognition Laboratory, Tecnológico de Estudios Superiores de Jocotitlán, Carretera Toluca-Atlacomulco KM 44.8, Ejido de San Juan y San Agustín, Jocotitlán 50700, Mexico
2
Computer Science, Universidad Autónoma del Estado de México, Carretera Toluca- Atlacomulco KM 60, Atlacomulco 50000, Mexico
3
Division of Graduate Studies and Research, Instituto Tecnológico de Toluca, Av. Tecnológico s/n. Colonia Agrícola Bellavista, Metepec, Edo. De México 52149, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Christian Dawson
Appl. Sci. 2016, 6(7), 200; https://doi.org/10.3390/app6070200
Received: 31 March 2016 / Revised: 22 June 2016 / Accepted: 23 June 2016 / Published: 11 July 2016
(This article belongs to the Special Issue Applied Artificial Neural Network)
In this work, we developed a Selective Dynamic Sampling Approach (SDSA) to deal with the class imbalance problem. It is based on the idea of using only the most appropriate samples during the neural network training stage. The “average samples”are the best to train the neural network, they are neither hard, nor easy to learn, and they could improve the classifier performance. The experimental results show that the proposed method is a successful method to deal with the two-class imbalance problem. It is very competitive with respect to well-known over-sampling approaches and dynamic sampling approaches, even often outperforming the under-sampling and standard back-propagation methods. SDSA is a very simple method for automatically selecting the most appropriate samples (average samples) during the training of the back-propagation, and it is very efficient. In the training stage, SDSA uses significantly fewer samples than the popular over-sampling approaches and even than the standard back-propagation trained with the original dataset. View Full-Text
Keywords: two-class imbalance problem; average samples; over-sampling; under-sampling; dynamic sampling two-class imbalance problem; average samples; over-sampling; under-sampling; dynamic sampling
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MDPI and ACS Style

Alejo, R.; Monroy-de-Jesús, J.; Pacheco-Sánchez, J.H.; López-González, E.; Antonio-Velázquez, J.A. A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem. Appl. Sci. 2016, 6, 200.

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