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Authors: Khalid M. Salama 1 and Ashraf M. Abdelbar 2

Affiliations: 1 University of Kent, United Kingdom ; 2 Brandon University, Canada

Keyword(s): Ant Colony Optimization (ACO), Machine Learning, Pattern Classification, Neural Networks.

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: Although artificial neural networks can be a very effective classification method, one of the drawbacks of their use is the need to manually prescribe the neural network topology. Recent work has introduced the ANN-Miner algorithm, an Ant Colony Optimization (ACO) technique for optimizing the topology of arbitrary FFNN's, i.e. FFNN's with multiple hidden layers, layer-skipping connections, and without the requirement of full-connectivity between successive layers. In this paper, we explore the use of several classification quality evaluation functions in ANN-Miner. Our experimental results, using 30 popular benchmark datasets, identify several quality functions that significantly improve on the simple Accuracy quality function that was previously used in ANN-Miner.

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Paper citation in several formats:
Salama, K. and Abdelbar, A. (2014). Exploring the Impact of Different Classification Quality Functions in an ACO Algorithm for Learning Neural Network Structures. In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - ECTA, (IJCCI 2014) ISBN 978-989-758-052-9, pages 137-144. DOI: 10.5220/0005031301370144

@conference{ecta14,
author={Khalid M. Salama. and Ashraf M. Abdelbar.},
title={Exploring the Impact of Different Classification Quality Functions in an ACO Algorithm for Learning Neural Network Structures},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - ECTA, (IJCCI 2014)},
year={2014},
pages={137-144},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005031301370144},
isbn={978-989-758-052-9},
}

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - ECTA, (IJCCI 2014)
TI - Exploring the Impact of Different Classification Quality Functions in an ACO Algorithm for Learning Neural Network Structures
SN - 978-989-758-052-9
AU - Salama, K.
AU - Abdelbar, A.
PY - 2014
SP - 137
EP - 144
DO - 10.5220/0005031301370144