Neural network training and rule extraction with augmented discretized input

• Main Authors: Hayashi, Yoichi, Setiono, Rudy, Azcarraga, Arnulfo P.
• Format: text
• Published: Animo Repository 2016
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/931
• Institution: De La Salle University

© 2016 Elsevier B.V. The classification and prediction accuracy of neural networks can be improved when they are trained with discretized continuous attributes as additional inputs. Such input augmentation makes it easier for the network weights to form more accurate decision boundaries when the dat...