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A Mixed-Attribute Approach in Ant-Miner Classification Rule Discovery Algorithm

Helal, Ayah, Otero, Fernando E.B. (2016) A Mixed-Attribute Approach in Ant-Miner Classification Rule Discovery Algorithm. In: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation. . pp. 13-20. ACM Press (doi:10.1145/2908812.2908900) (KAR id:55150)

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http://dx.doi.org/10.1145/2908812.2908900

Abstract

In this paper, we introduce Ant-MinerMA to tackle mixed-attribute classification problems. Most classification problems involve continuous, ordinal and categorical attributes. The majority of Ant Colony Optimization (ACO) classification algorithms have the limitation of being able to handle categorical attributes only, with few exceptions that use a discretisation procedure when handling continuous attributes either in a preprocessing stage or during the rule creation. Using a solution archive as a pheromone model, inspired by the ACO for mixed-variable optimization (ACO-MV), we eliminate the need for a discretisation procedure and attributes can be treated directly as continuous, ordinal, or categorical. We compared the proposed Ant-MinerMA against cAnt-Miner, an ACO-based classification algorithm that uses a discretisation procedure in the rule construction process. Our results show that Ant-MinerMA achieved significant improvements on computational time due to the elimination of the discretisation procedure without affecting the predictive performance.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1145/2908812.2908900
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: Fernando Otero
Date Deposited: 26 Apr 2016 09:40 UTC
Last Modified: 16 Feb 2021 13:34 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/55150 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
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