You are not logged in.

A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction

Pourpanah, Farhad, Lim, Chee Peng and Mohamad Saleh, Junita 2016, A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction, Expert systems with applications, vol. 49, pp. 74-85, doi: 10.1016/j.eswa.2015.11.009.

Attached Files
Name Description MIMEType Size Downloads

Title A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction
Author(s) Pourpanah, Farhad
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Mohamad Saleh, Junita
Journal name Expert systems with applications
Volume number 49
Start page 74
End page 85
Total pages 12
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-05-01
ISSN 0957-4174
Keyword(s) Fuzzy ARTMAP
reinforcement learning
Q-learning
data classification
rule extraction
genetic algorithm
Summary A two-stage hybrid model for data classification and rule extraction is proposed. The first stage uses a Fuzzy ARTMAP (FAM) classifier with Q-learning (known as QFAM) for incremental learning of data samples, while the second stage uses a Genetic Algorithm (GA) for rule extraction from QFAM. Given a new data sample, the resulting hybrid model, known as QFAM-GA, is able to provide prediction pertaining to the target class of the data sample as well as to give a fuzzy if-then rule to explain the prediction. To reduce the network complexity, a pruning scheme using Q-values is applied to reduce the number of prototypes generated by QFAM. A 'don't care' technique is employed to minimize the number of input features using the GA. A number of benchmark problems are used to evaluate the effectiveness of QFAM-GA in terms of test accuracy, noise tolerance, model complexity (number of rules and total rule length). The results are comparable, if not better, than many other models reported in the literature. The main significance of this research is a usable and useful intelligent model (i.e., QFAM-GA) for data classification in noisy conditions with the capability of yielding a set of explanatory rules with minimum antecedents. In addition, QFAM-GA is able to maximize accuracy and minimize model complexity simultaneously. The empirical outcome positively demonstrate the potential impact of QFAM-GA in the practical environment, i.e., providing an accurate prediction with a concise justification pertaining to the prediction to the domain users, therefore allowing domain users to adopt QFAM-GA as a useful decision support tool in assisting their decision-making processes.
Language eng
DOI 10.1016/j.eswa.2015.11.009
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083078

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 8 times in TR Web of Science
Scopus Citation Count Cited 8 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 114 Abstract Views, 6 File Downloads  -  Detailed Statistics
Created: Wed, 27 Apr 2016, 09:07:29 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.