Particle swarm and bayesian networks applied to attribute selection for protein functional classification.

Correa, E.S. and Freitas, A.A. and Johnson, Colin G. (2007) Particle swarm and bayesian networks applied to attribute selection for protein functional classification. In: Yu, T., ed. Genetic And Evolutionary Computation Conference. ACM pp. 2651-2658. ISBN 978-1-59593-698-1. (The full text of this publication is not available from this repository)

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Abstract

The Discrete Particle Swarm (DPSO) algorithm is an optimizationmethod that belongs to the fertile paradigm of Swarm Intelligence. The DPSO was designed for the task of attribute selection and it deals with discrete variables in a straightforward manner. This work extends the DPSO algorithm in two ways. First, we enable the DPSO to select attributes for a Bayesian network algorithm, which is a much more sophisticated algorithm than the Naive Bayes classifier previously used by this algorithm. Second, we apply the DPSO to a challenging protein functional classification data set, involving a large number of classes to be predicted. The performance of the DPSO is compared to the performance of a Binary PSO on the task of selecting attributes in this challenging data set. The criteria used for comparison are: (1) maximizing predictive accuracy; and (2) finding the smallest subset of attributes.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: particle swarm optimization, data mining, classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:04
Last Modified: 17 Jul 2012 14:36
Resource URI: http://kar.kent.ac.uk/id/eprint/14573 (The current URI for this page, for reference purposes)
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