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Automatic design of ant-miner mixed attributes for classification rule discovery

Helal, Ayah and Otero, Fernando E.B. (2017) Automatic design of ant-miner mixed attributes for classification rule discovery. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO Genetic and Evolutionary Computation Conference . ACM, New York, USA, pp. 433-440. ISBN 978-1-4503-4920-8. (doi:10.1145/3071178.3071306)

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http://dx.doi.org/10.1145/3071178.3071306

Abstract

Ant-Miner Mixed Attributes (Ant-MinerMA) was inspired and built based on ACOMV. which uses an archive-based pheromone model to cope with mixed attribute types. On the one hand, the use of an archive-based pheromone model improved significantly the runtime of Ant-MinerMA and helped to eliminate the need for discretisation procedure when dealing with continuous attributes. On the other hand, the graph-based pheromone model showed superiority when dealing with datasets containing a large size of attributes, as the graph helps the algorithm to easily identify good attributes. In this paper, we propose an automatic design framework to incorporate the graph-based model along with the archive-based model in the rule creation process. We compared the automatically designed hybrid algorithm against existing ACO-based algorithms: one using a graph-based pheromone model and one using an archive-based pheromone model. Our results show that the hybrid algorithm improves the predictive quality over both the base archive-based and graph-based algorithms.

Item Type: Book section
DOI/Identification number: 10.1145/3071178.3071306
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: 03 Jul 2017 10:58 UTC
Last Modified: 24 Sep 2019 08:49 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/62176 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
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