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Using genetic algorithm to select the presentation order of training patterns that improves simplified fuzzy ARTMAP classification performance

Palaniappan, Ramaswamy, Eswaran, C. (2009) Using genetic algorithm to select the presentation order of training patterns that improves simplified fuzzy ARTMAP classification performance. Applied Soft Computing, 9 (1). pp. 100-106. ISSN 1568-4946. (doi:10.1016/j.asoc.2008.03.003) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1016/j.asoc.2008.03.003

Abstract

The presentation order of training patterns to a simplified fuzzy ARTMAP (SFAM) neural network affects the classification performance. The common method to solve this problem is to use several simulations with training patterns presented in random order, where voting strategy is used to compute the final performance. Recently, an ordering method based on min-max clustering was introduced to select the presentation order of training patterns based on a single simulation. In this paper, another single simulation method based on genetic algorithm is proposed to obtain the presentation order of training patterns for improving the performance of SFAM. The proposed method is applied to a 40-class individual classification problem using visual evoked potential signals and three other datasets from UCI repository. The proposed method has the advantages of improved classification performance, smaller network size and lower training time compared to the random ordering and min-max methods. When compared to the random ordering method, the new ordering scheme has the additional advantage of requiring only a single simulation. As the proposed method is general, it can also be applied to a fuzzy ARTMAP neural network when it is used as a classifier. © 2008 Elsevier B.V. All rights reserved.

Item Type: Article
DOI/Identification number: 10.1016/j.asoc.2008.03.003
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Appl. Soft Comput. J. [Field not mapped to EPrints] AD - Department of Computing and Electronic Systems, University of Essex, Colchester, United Kingdom [Field not mapped to EPrints] AD - Faculty of Information Technology, Multimedia University, Cyberjaya, Malaysia [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Article [Field not mapped to EPrints]
Uncontrolled keywords: Fuzzy ARTMAP, Genetic algorithm, Individual identification, Min-max ordering, Visual evoked potential, Voting strategy, Algorithms, Boolean functions, Genetic algorithms, Image classification, Network protocols, Neural networks, Sensor networks, Systems engineering, Fuzzy ARTMAP, Genetic algorithm, Individual identification, Min-max ordering, Visual evoked potential, Voting strategy, Random processes
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 12 Dec 2018 22:20 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70713 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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