Paper Info
Title
Greedy rule generation from discrete data and its use in neural network rule extraction.
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.
Year
DOI
Venue
2008
10.1016/j.neunet.2008.01.003
Neural Networks
Keywords
Field
DocType
neural network,neural networks,best rule,proposed grg algorithm,neural network rule extraction,discrete attribute,rule set,greedy rule generation,discrete data,classification rule,classification problem,rule generation,classification,grg method,discretization,clustering,decision tree,decision trees,neural nets,greedy algorithms,generic algorithm
Decision tree,Discretization,Delta rule,Subspace topology,Pattern recognition,Computer science,Greedy algorithm,Artificial intelligence,Artificial neural network,Machine learning
Journal
Volume
Issue
ISSN
21
7
0893-6080
Citations 
PageRank 
References 
11
0.56
25
Authors
4
Name
Order
Citations
PageRank
Koichi Odajima1110.56
Y Hayashi242325.25
Gong Tianxia3110.56
R Setiono42143230.39