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
An Empirical Study for the Multi-class Imbalance Problem with Neural Networks
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A Preliminar Analysis of CO2RBFN in Imbalanced Problems
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A learning method for the class imbalance problem with medical data sets
Computers in Biology and Medicine
IEEE Transactions on Neural Networks
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets
Pattern Recognition Letters
Multi-agent based classification using argumentation from experience
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Multi-agent based classification using argumentation from experience
Autonomous Agents and Multi-Agent Systems
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Recently, the class imbalance problem in neural networks, is receiving growing attention in works of machine learning and data mining. This problem appears when the samples of some classes are much smaller than those in the other classes. The classes with small size can be ignored in the learning process and the convergence of these classes is very slow. This paper studies empirically the class imbalance problem in the context of the RBF neural network trained with backpropagation algorithm. We propose to introduce a cost function in the training process to compensate imbalance class and one strategy to reduce the impact of the cost function in the data probability distribution.