EAST: An Exponential Adaptive Skipping Training Algorithm for Multilayer Feedforward Neural Networks

Multilayer Feedforward Neural Network (MFNN) has been administered widely for solving a wide range of supervised pattern recognition tasks. The major problem in the MFNN training phase is its long training time especially when it is trained on very huge training datasets. In this accordance, an enhanced training algorithm called Exponential Adaptive Skipping Training (EAST) Algorithm is proposed in this research paper which intensifies on reducing the training time of the MFNN through stochastic manifestation of training datasets. The stochastic manifestation is accomplished by partitioning the training dataset into two completely separate classes, classified and misclassified class, based on the comparison result of the calculated error measure with the threshold value. Only the input samples in the misclassified class are exhibited to the MFNN for training in the next epoch, whereas the correctly classified class is skipped exponentially which dynamically reducing the number of training input samples exhibited at every single epoch. Thus decreasing the size of the training dataset exponentially can reduce the total training time, thereby speeding up the training process. This EAST algorithm can be integrated with any supervised training algorithms and also it is very simple to implement. The evaluation of the proposed EAST algorithm is demonstrated effectively using the benchmark datasets Iris, Waveform, Heart Disease and Breast Cancer for different learning rate. Simulation study proved that EAST training algorithm results in faster training than LAST and standard BPN algorithm. Key-Words: Adaptive Skipping, Neural Network, Training Algorithm, Training Speed, MFNN, Learning Rate