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: 6th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, March 26th - 28th 2008, Naples, Italy. (The full text of this publication is not available from this repository)

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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: Conference or workshop item (Paper)
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 > Science Technology and Medical Studies > School of Computing
Depositing User: Jane Griffiths
Date Deposited: 20 Apr 2009 13:08
Last Modified: 15 Jul 2014 11:24
Resource URI: http://kar.kent.ac.uk/id/eprint/15684 (The current URI for this page, for reference purposes)
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