A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data

Holden, Nicholas and Freitas, Alex A. (2005) A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data. In: Arabshahi, Payman and Martinoli, Alcherio, eds. Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. IEEE, pp. 100-107. ISBN 0-7803-8916-6. (doi:10.1109/SIS.2005.1501608) (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:14309)

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/10.1109/SIS.2005.1501608

Abstract

This paper proposes a hybrid PSO/ACO algorithm for hierarchical classification, where the classes to be predicted are arranged in a tree-like hierarchy. The performance of the algorithm is evaluated on a challenging biological data set, involving the hierarchical functional classification of enzymes. The proposed algorithm is compared with an existing PSO for classification, which was also adapted for hierarchical classification.

Item Type:	Book section
DOI/Identification number:	10.1109/SIS.2005.1501608
Uncontrolled keywords:	data mining, classification, bioinformatics, ant colony optimization, particle swarm optimization
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 18:03 UTC
Last Modified:	05 Nov 2024 09:48 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/14309 (The current URI for this page, for reference purposes)

University of Kent Author Information

Freitas, Alex A..

Creator's ORCID:	https://orcid.org/0000-0001-9825-4700
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