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Authors: Khalid M. Salama and Fernando E. B. Otero

Affiliation: University of Kent, United Kingdom

ISBN: 978-989-758-052-9

Keyword(s): Ant Colony Optimization (ACO), Data Mining, Classification, Decision Trees, Multi-trees.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Swarm/Collective Intelligence ; Symbolic Systems

Abstract: Ant Colony Optimization (ACO) is a meta-heuristic for solving combinatorial optimization problems, inspired by the behaviour of biological ant colonies. One of the successful applications of ACO is learning classification models (classifiers). A classifier encodes the relationships between the input attribute values and the values of a class attribute in a given set of labelled cases and it can be used to predict the class value of new unlabelled cases. Decision trees have been widely used as a type of classification model that represent comprehensible knowledge to the user. In this paper, we propose the use of ACO-based algorithms for learning an extended multi-tree classification model, which consists of multiple decision trees, one for each class value. Each class-based decision trees is responsible for discriminating between its class value and all other values available in the class domain. Our proposed algorithms are empirically evaluated against well-known decision trees induct ion algorithms, as well as the ACO-based Ant-Tree-Miner algorithm. The results show an overall improvement in predictive accuracy over 32 benchmark datasets. We also discuss how the new multi-tree models can provide the user with more understanding and knowledge-interpretability in a given domain. (More)

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Paper citation in several formats:
Salama K. and Otero F. (2014). Learning Multi-tree Classification Models with Ant Colony Optimization.In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014) ISBN 978-989-758-052-9, pages 38-48. DOI: 10.5220/0005071300380048

@conference{ecta14,
author={Khalid M. Salama and Fernando E. B. Otero},
title={Learning Multi-tree Classification Models with Ant Colony Optimization},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)},
year={2014},
pages={38-48},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005071300380048},
isbn={978-989-758-052-9},
}

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)
TI - Learning Multi-tree Classification Models with Ant Colony Optimization
SN - 978-989-758-052-9
AU - Salama K.
AU - Otero F.
PY - 2014
SP - 38
EP - 48
DO - 10.5220/0005071300380048

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