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An advanced combination strategy for multi-classifiers employed in large multi-class problem domains

Lorrentz, Pierre, Howells, Gareth, McDonald-Maier, Klaus D. (2011) An advanced combination strategy for multi-classifiers employed in large multi-class problem domains. Journal of Applied Soft Computing, 11 (2). pp. 2151-2163. ISSN 1568-4946. (doi:10.1016/j.asoc.2010.07.014) (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.2010.07.014

Abstract

Traditional artificial neural architectures possess limited ability to address the scale problem exhibited by a large number of distinct pattern classes and limited training data. To address these problems, this paper explores a novel advanced encoding scheme, which reduces both memory demand and execution time, and provides improved performance. The novel advanced encoding scheme known as the engine encoding, have been implemented in a multi-classifier, which combines the scaled probabilities, configuration information, and the discriminating abilities of the associated component classifiers. The problems of overloading and saturation experienced by traditional networks are solved by training the base classifiers on differing sub-sets of the required pattern classes and allowing the combiner classifier to derive a solution. Current Multi-classifier Systems are easily biased when trained on one class more often than another class, when patterns representing a class are very large compared to the rest, or when the multi-classifier depends on a certain fixed order of arrangement of pattern classes. A unique statistical arrangement method is hereby presented which aims to solve the bias problem. This statistical arrangement method also enhances independence of component classifiers. The system is demonstrated on the exemplar of fingerprint identification and utilizes a Weightless Neural System called the Enhanced Probabilistic Convergent Neural Network (EPCN) in a Multi-classifier System.

Item Type: Article
DOI/Identification number: 10.1016/j.asoc.2010.07.014
Uncontrolled keywords: Engine encoding; Intelligent Combination; Multi-classifier fusion; Statistical arrangement
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Tina Thompson
Date Deposited: 28 Oct 2013 11:17 UTC
Last Modified: 29 May 2019 11:10 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/35688 (The current URI for this page, for reference purposes)
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