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Learning Multi-Tree Classification Models with Ant Colony Optimization

Salama, Khalid M., Otero, Fernando E.B. (2014) Learning Multi-Tree Classification Models with Ant Colony Optimization. In: Proceedings of the International Conference on Evolutionary Computation Theory and Applications. . pp. 38-48. INSTICC Press ISBN 978-989-758-052-9. (doi:10.5220/0005071300380048) (KAR id:42147)

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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 induction 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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.5220/0005071300380048
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Fernando Otero
Date Deposited: 07 Aug 2014 19:57 UTC
Last Modified: 16 Feb 2021 12:54 UTC
Resource URI: (The current URI for this page, for reference purposes)
Otero, Fernando E.B.:
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