Abstract
This work presents a new classification method based on the iterative combination of two steps: a clustering technique and a set of one-layer neural networks. First, the clustering algorithm divides the input space in several regions (local models). Subsequently, a one-layer neural network, for each local region, is used to fit the model (classifier) for a specific group of data points. Experimental results on three different data sets are showed to verify the validity of the proposed method. Besides, a comparative study with a feedforward neural network is included. This study exhibits that the presented algorithm is a fast procedure that obtains, in many cases, better results than the other technique.
This work has been funded by the project TIC2003-00600 of the Ministerio de Ciencia y Tecnología, Spain (partially supported by FEDER funds.)
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© 2006 Springer-Verlag Berlin Heidelberg
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Platero-Santos, S., Fontenla-Romero, O., Alonso-Betanzos, A. (2006). A Fast Classification Algorithm Based on Local Models. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_30
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DOI: https://doi.org/10.1007/11875581_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
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