King's College London

Research portal

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

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Ayah Helal, Fernando E.B. Otero

Original languageUndefined/Unknown
Title of host publicationProceedings of the Genetic and Evolutionary Computation Conference
Place of PublicationNew York, USA
PublisherACM
Pages433-440
Number of pages8
DOIs
E-pub ahead of printJul 2017

Publication series

NameGECCO Genetic and Evolutionary Computation Conference
PublisherACM

Documents

King's Authors

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.

Download statistics

No data available

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454