Optimized Feature Extraction and the Bayes Decision in Feed-Forward Classifier Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three learning phases for radial-basis-function networks
Neural Networks
Neural Learning from Unbalanced Data
Applied Intelligence
Efficient training of RBF neural networks for pattern recognition
IEEE Transactions on Neural Networks
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets
Pattern Recognition Letters
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When training set is unbalanced, the conventional least square error (LSE) training strategy is less efficient to train neural network (NN) for classification because it often lead the NN to overcompensate for the dominant group. Therefore, in this paper a dynamic threshold learning algorithm (DTLA) is proposed as the substitute for the conventional LSE algorithm. This method uses multiple dynamic threshold parameters to gradually remove some training patterns that can be classified correctly by current Radial Basis Function (RBF) network out of the training set during training process, which changes the unbalanced training problem into a balanced training problem and improves the classification rate of the small group. Moreover, we use the dynamical threshold learning algorithm to classify the remote sensing images, when the unbalanced level of classes is high, a good effect is obtained.