Paper Info
Title
An improved dynamic sampling back-propagation algorithm based on mean square error to face the multi-class imbalance problem.
Abstract
In this paper, we present an improved dynamic sampling approach (I-SDSA) for facing the multi-class imbalance problem. I-SDSA is a modification of the back-propagation algorithm, which is focused to make a better use of the training samples for improving the classification performance of the multilayer perceptron (MLP). I-SDSA uses the mean square error and a Gaussian function to identify the best samples to train the neural network. Results shown in this article stand out that I-SDSA makes better exploitation of the training dataset and improves the MLP classification performance. In others words, I-SDSA is a successful technique for dealing with the multi-class imbalance problem. In addition, results presented in this work indicate that the proposed method is very competitive in terms of classification performance with respect to classical over-sampling methods (also, combined with well-known features selection methods) and other dynamic sampling approaches, even in training time and size it is better than the over-sampling methods .
Year
DOI
Venue
2017
10.1007/s00521-017-2938-3
Neural Computing and Applications
Keywords
Field
DocType
MLP, Back-propagation, Multi-class imbalance problem, Mean square error, Re-sampling methods, Dynamic sampling
Back propagation algorithm,Pattern recognition,Computer science,Mean squared error,Multilayer perceptron,Sampling (statistics),Artificial intelligence,Artificial neural network,Backpropagation,Gaussian function,Machine learning
Journal
Volume
Issue
ISSN
28
10
0941-0643
Citations 
PageRank 
References 
0
0.34
37
Authors
4
Name
Order
Citations
PageRank
R. Alejo115810.40
Juan Monroy-de-Jesús200.34
J. C. Ambriz-Polo300.34
J. H. Pacheco-Sanchez4202.02