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

Correa, Elon S. and Freitas, Alex A. and Johnson, Colin G. (2007) Particle swarm and bayesian networks applied to attribute selection for protein functional classification. In: Yu, Tina, ed. GECCO '07 Proceedings of the 9th annual conference companion on Genetic and evolutionary computation. GECCO Genetic and Evolutionary Computation Conference . ACM, New York, USA, pp. 2651-2658. ISBN 978-1-59593-698-1. (doi:10.1145/1274000.1274081) (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) (KAR id:14573)

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.
Official URL:
http://dx.doi.org/10.1145/1274000.1274081

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: Book section
DOI/Identification number: 10.1145/1274000.1274081
Uncontrolled keywords: particle swarm optimization, data mining, classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:04 UTC
Last Modified: 16 Nov 2021 09:52 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14573 (The current URI for this page, for reference purposes)

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