Fuzzy ARTMAP and a hybrid chaos genetic algorithm for medical pattern classification

Loo, Chin, Tan, Shing and Lim, Chee 2009, Fuzzy ARTMAP and a hybrid chaos genetic algorithm for medical pattern classification, Australian journal of intelligent information processing systems, vol. 10, no. 4, pp. 12-21.

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Title Fuzzy ARTMAP and a hybrid chaos genetic algorithm for medical pattern classification
Author(s) Loo, Chin
Tan, Shing
Lim, CheeORCID iD for Lim, Chee orcid.org/0000-0003-4191-9083
Journal name Australian journal of intelligent information processing systems
Volume number 10
Issue number 4
Start page 12
End page 21
Total pages 10
Publisher Australian National University, Department of Computer Science, College of Engineering and Computer Science
Place of publication Canberra, A. C. T.
Publication date 2009
ISSN 1321-2133
Keyword(s) Fuzzy ARTMAP
Neural network
Hybrid Chaos
Genetic A
Summary In this paper, an Evolutionary Artificial Neural Network (EANN), which combines the Fuzzy ARTMAP (FAM) neural network and a hybrid Chaos Genetic Algorithm (CGA), is proposed for undertaking pattern classification tasks. The hybrid CGA is a modified version of the hybrid real-coded genetic algorithms that includes a Chaotic Mapping Operator (CMO) in its search and adaptation process. It is used to evolve the connection weights in FAM, and the resulting EANN is known as FAM-hybrid CGA. The CMO in the hybrid CGA is used to generate a group of chromosomes that incorporates the characteristics of chaos. The chromosomes are then adapted with an arbitrary small amount of variation in every generation. As the evolution procedure proceeds, chromosomes with considerable differences are produced. Such chromosomes, which are located at different regions of interest in the solution space, are able to provide good solutions to undertake search and adaption problems. The effectiveness of the proposed FAM-hybrid CGA model is first evaluated using benchmark medical data sets from the UCI machine learning repository. Its applicability to medical decision support is then demonstrated using a real database of patient records with suspected Acute Coronary Syndrome. The results indicate that FAM-hybrid CGA is able to outperform its neural network counterpart (i.e., FAM), and it can be employed as a useful pattern classification tool for tackling medical decision support tasks.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048495

Document type: Journal Article
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