MofN rule extraction from neural networks trained with augmented discretized input

• Main Authors: Setiono, Rudy, Azcarraga, Arnulfo P., Hayashi, Yoichi
• Format: text
• Published: Animo Repository 2014
• Subjects: Neural networks (Computer science); Data sets; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/4427
• Institution: De La Salle University

The 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 data samples of different classes in the data set are...