Incremental Ordered Neural Network Training
Sheng-Uei Guan, Jun Liu · Journal of Intelligent Systems · 2002
This paper investigates the incremental training of a Neural Network (NN) with the input attributes introduped in order.A specially designed NN is used to evaluate the individual discrimination ability of each input attribute.Attributes are then sorted in descending, ascending, and random orders of their individual discrimination abilities and introduced into another NN being trained with an incremental training algorithm, ITID.To reduce the interference caused by irrelevant features and high-complexity tasks, only relevant features are involved and tasks are decomposed in the experiments.The experimental results of several benchmark problems show that descending order obtains the highest generalization accuracy among the three training orders for both classification and regression problems.