Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Neural networks for pattern recognition
Neural networks for pattern recognition
Classification of imbalanced remote-sensing data by neural networks
Pattern Recognition Letters - special issue on pattern recognition in practice V
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
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
IEEE Transactions on Knowledge and Data Engineering
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
Controlling multi-class error rates for MLP classifier by bias adjustment based on penalty matrix
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
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The multi-class imbalance problem in supervised pattern recognition methods is receiving growing attention. Imbalanced datasets means that some classes are represented by a large number of samples while the others classes only contain a few. In real-world applications, imbalanced training sets may produce an important deterioration of the classifier performance when neural networks are applied in the classes less represented. In this paper we propose training cost-sentitive neural networks with editing techniques for handling the class imbalance problem on multi-class datasets. The aim is to remove majority samples while compensating the class imbalance during the training process. Experiments with real data sets demonstrate the effectiveness of the strategy here proposed.