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Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization

Helal, Ayah, Brookhouse, James, Otero, Fernando E.B. (2018) Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization. In: 2018 IEEE Congress on Evolutionary Computation (CEC). . pp. 1-7. IEEE ISBN 978-1-5090-6018-4. E-ISBN 978-1-5090-6017-7. (doi:10.1109/CEC.2018.8477643)

Abstract

In this paper we introduce a new algorithm, called Ant-Miner-Reg_MA, to tackle the regression problem using an archive-based pheromone model. Existing regression algorithms handle continuous attribute using a discretisation procedure, either in a preprocessing stage or during rule creation. Using an archive as a pheromone model, inspired by the ACO for Mixed-Variable (ACO_MV), we eliminate the need for a discretisation procedure. We compare the proposed Ant-Miner-Reg_MA against Ant-Miner-Reg, an ACO-based regression algorithm that uses a dynamic discretisation procedure, inspired on M5 algorithm, during rule construction process. Our results show that Ant-Miner-Reg_MA achieved a significant improvement in the relative root mean square error of the models created, overcoming the limitations of the dynamic discretisation procedure.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/CEC.2018.8477643
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 31 May 2018 09:54 UTC
Last Modified: 01 Aug 2019 10:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/67178 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
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