Resampling methods versus cost functions for training an MLP in the class imbalance context

Authors:
R. Alejo;P. Toribio;J. M. Sotoca;R. M. Valdovinos;E. Gasca
Affiliations:
Tecnológico de Estudios Superiores de Jocotitlán, col. Ejido de San Juan y San Agustín, Jocotitlán;Tecnológico de Estudios Superiores de Jocotitlán, col. Ejido de San Juan y San Agustín, Jocotitlán;Institute of New Imaging Technologies, Universitat Jaume I, Castelló de la Plana, Spain;Centro Universitario UAEM Valle de Chalco, Universidad Autónoma del Estado de México, Valle de Chalco, Mexico;Lab. Reconocimiento de Patrones, Instituto Tecnológico de Toluca, Metepec, México
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Year:
2011

Citing 9
Cited 0

Classification of imbalanced remote-sensing data by neural networks

Pattern Recognition Letters - special issue on pattern recognition in practice V
Machine Learning for the Detection of Oil Spills in Satellite Radar Images

Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Neural Network Classification and Prior Class Probabilities

Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem

IEEE Transactions on Knowledge and Data Engineering
The class imbalance problem: A systematic study

Intelligent Data Analysis
Learning from Imbalanced Data

IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique

Journal of Artificial Intelligence Research
A novelty detection approach to classification

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Efficient classification for multiclass problems using modular neural networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The class imbalance problem has been studied from different approaches, some of the most popular are based on resizing the data set or internally basing the discrimination-based process. Both methods try to compensate the class imbalance distribution, however, it is necessary to consider the effect that each method produces in the training process of theMultilayer Perceptron (MLP). The experimental results shows the negative and positive effects that each of these approaches has on the MLP behavior.