Abstract
Error Correcting Output Codes reveal an efficient strategy in dealing with multi-class classification problems. According to this technique, a multi-class problem is decomposed into several binary ones. On these created sub-problems we apply binary classifiers and then, by combining the acquired solutions, we are able to solve the initial multi-class problem. In this paper we consider the optimization of the Linear Discriminant Error Correcting Output Codes framework using Particle Swarm Optimization. In particular, we apply the Particle Swarm Optimization algorithm in order to optimally select the free parameters that control the split of the initial problem’s classes into sub-classes. Moreover, by using the Support Vector Machine as classifier we can additionally apply the Particle Swarm Optimization algorithm to tune its free parameters. Our experimental results show that by applying Particle Swarm Optimization on the Sub-class Linear Discriminant Error Correcting Output Codes framework we get a significant improvement in the classification performance.
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Bouzas, D., Arvanitopoulos, N., Tefas, A. (2011). Optimizing Linear Discriminant Error Correcting Output Codes Using Particle Swarm Optimization. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_11
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DOI: https://doi.org/10.1007/978-3-642-21738-8_11
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