Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/99308
Title: Improving backpropagation learning with feature selection
Authors: Setiono, R. 
Liu, H. 
Keywords: Backpropagation
Feature selection
Feedforward neural network
Information theory
Issue Date: 1996
Source: Setiono, R.,Liu, H. (1996). Improving backpropagation learning with feature selection. Applied Intelligence 6 (2) : 129-139. ScholarBank@NUS Repository.
Abstract: There exist redundant, irrelevant and noisy data. Using proper data to train a network can speed up training, simplify the learned structure, and improve its performance. A two-phase training algorithm is proposed. In the first phase, the number of input units of the network is determined by using an information base method. Only those attributes that meet certain criteria for inclusion will be considered as the input to the network. In the second phase, the number of hidden units of the network is selected automatically based on the performance of the network on the training data. One hidden unit is added at a time only if it is necessary. The experimental results show that this new algorithm can achieve a faster learning time, a simpler network and an improved performance. © 1996 Kluwer Academic Publishers.
Source Title: Applied Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/99308
ISSN: 0924669X
Appears in Collections:Staff Publications

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