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

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #67178 Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization
sameAs	Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization
http://eprints.org/ontology/hasAccepted	Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization (PDF)
http://eprints.org/ontology/hasDocument	Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization (PDF) Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization (Other) Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization (Other) Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization (Other) Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization (Other) Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization (Other)
dc:hasVersion	Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization (PDF)
Title	Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2018-07-08
Creator	Ayah Helal Fernando E.B. Otero James Brookhouse
status	peer reviewed published
Publisher	IEEE
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.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/67178#authors
presented at	2018 IEEE Congress on Evolutionary Computation
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #67178 Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization

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