A fast classification algorithm based on local models

Authors:
Sabela Platero-Santos;Oscar Fontenla-Romero;Amparo Alonso-Betanzos
Affiliations:
Department of Computer Science, University of A Coruña, Facultad de Informática, A Coruña, Spain;Department of Computer Science, University of A Coruña, Facultad de Informática, A Coruña, Spain;Department of Computer Science, University of A Coruña, Facultad de Informática, A Coruña, Spain
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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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.