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Improving the performance of hierarchical classification with swarm intelligence

Holden, Nicholas and Freitas, Alex A. (2008) Improving the performance of hierarchical classification with swarm intelligence. In: Marchiori, Elena and Moore, Jason H., eds. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 6th European Conference. Lecture Notes in Computer Science, 4973 . Springer, Berlin, Germany, pp. 48-60. ISBN 978-3-540-78756-3. E-ISBN 978-3-540-78757-0. (doi:10.1007/978-3-540-78757-0_5) (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/10.1007/978-3-540-78757-0_5

Abstract

In this paper we propose a new method to improve the performance of hierarchical classification. We use a swarm intelligence algorithm to select the type of classification algorithm to be used at each "classifier node" in a classifier tree. These classifier nodes are used in a top-down divide and conquer fashion to classify the examples from hierarchical data sets. In this paper we propose a swarm intelligence based approach which attempts to mitigate a major drawback with a recently proposed local search-based, greedy algorithm. Our swarm intelligence based approach is able to take into account classifier interactions whereas the greedy algorithm is not. We evaluate our proposed method against the greedy method in four challenging bioinformatics data sets and find that, overall, there is a significant increase in performance.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-540-78757-0_5
Uncontrolled keywords: particle swarm optimisation; ant colony optimisation; data mining; hierarchical classification; protein function prediction
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Faculties > Sciences > School of Computing
Depositing User: Jane Griffiths
Date Deposited: 20 Apr 2009 13:08 UTC
Last Modified: 29 May 2019 13:10 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/15684 (The current URI for this page, for reference purposes)
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