Please use this identifier to cite or link to this item:
Title: Greedy rule generation from discrete data and its use in neural network rule extraction
Authors: Odajima, K.
Hayashi, Y.
Tianxia, G.
Setiono, R. 
Keywords: Classification
Neural networks
Rule generation
Issue Date: 2008
Citation: Odajima, K., Hayashi, Y., Tianxia, G., Setiono, R. (2008). Greedy rule generation from discrete data and its use in neural network rule extraction. Neural Networks 21 (7) : 1020-1028. ScholarBank@NUS Repository.
Abstract: This paper proposes a GRG (Greedy Rule Generation) algorithm, a new method for generating classification rules from a data set with discrete attributes. The algorithm is "greedy" in the sense that at every iteration, it searches for the best rule to generate. The criteria for the best rule include the number of samples and the size of subspaces that it covers, as well as the number of attributes in the rule. This method is employed for extracting rules from neural networks that have been trained and pruned for solving classification problems. The classification rules are extracted from the neural networks using the standard decompositional approach. Neural networks with one hidden layer are trained and the proposed GRG algorithm is applied to their discretized hidden unit activation values. Our experimental results show that neural network rule extraction with the GRG method produces rule sets that are accurate and concise. Application of GRG directly on three medical data sets with discrete attributes also demonstrates its effectiveness for rule generation. © 2008 Elsevier Ltd. All rights reserved.
Source Title: Neural Networks
ISSN: 08936080
DOI: 10.1016/j.neunet.2008.01.003
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Oct 17, 2018


checked on Oct 9, 2018

Page view(s)

checked on Oct 13, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.