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A New Ant Colony Algorithm for Multi-Label Classification with Applications in Bioinformatics

Chan, Allen and Freitas, Alex A. (2006) A New Ant Colony Algorithm for Multi-Label Classification with Applications in Bioinformatics. In: Keijzer, Maarten, ed. Proceedings of the 8th annual conference on Genetic and evolutionary computation. ACM, New York, USA, pp. 27-34. ISBN 1-59593-186-4. (doi:10.1145/1143997.1144002) (KAR id:14459)

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http://dx.doi.org/10.1145/1143997.1144002

Abstract

The conventional classification task of data mining can be called single-label classification, since there is a single class attribute to be predicted. This paper addresses a more challenging version of the classification task, where there are two or more class attributes to be predicted. We propose a new ant colony algorithm for the multi-label classification task. The new algorithm, called MuLAM (Multi-Label Ant-Miner) is a major extension of Ant-Miner, the first ant colony algorithm for discovering classification rules. We report results comparing the performance of MuLAM with the performance of three other classification techniques, namely the very simple majority classifier, the original Ant-Miner algorithm and C5.0, a very popular rule induction algorithm. The experiments were performed using five bioinformatics datasets, involving the prediction of several kinds of protein function.

Item Type: Book section
DOI/Identification number: 10.1145/1143997.1144002
Uncontrolled keywords: data mining, bioinformatics, classification, ant colony 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 18:04 UTC
Last Modified: 16 Feb 2021 12:25 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14459 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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