Gyan: A Methodology for Rule Extraction from Artificial Neural Networks. A data mining and machine learning apporach
Nayak, Richi (2000) Gyan: A Methodology for Rule Extraction from Artificial Neural Networks. A data mining and machine learning apporach. .
Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures.
The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system.
A multi-stage methodology gyan is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of gyan is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Additional Information:||Access to manuscript currently restricted. Please contact author: firstname.lastname@example.org for more information.|
|Keywords:||data mining, neural networks, rule extraction|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Department:||Department of Computer Science|
|Deposited On:||05 Jul 2005|
|Last Modified:||09 Jun 2010 22:25|
Repository Staff Only: item control page