A Comparative Study of Local Classifiers Based on Clustering Techniques and One-Layer Neural Networks

  • Yuridia Gago-Pallares
  • Oscar Fontenla-Romero
  • Amparo Alonso-Betanzos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)


In this article different approximations of a local classifier algorithm are described and compared. The classification algorithm is composed by two different steps. The first one consists on the clustering of the input data by means of three different techniques, specifically a k-means algorithm, a Growing Neural Gas (GNG) and a Self-Organizing Map (SOM). The groups of data obtained are the input to the second step of the classifier, that is composed of a set of one-layer neural networks which aim is to fit a local model for each cluster. The three different approaches used in the first step are compared regarding several parameters such as its dependence on the initial state, the number of nodes employed and its performance. In order to carry out the comparative study, two artificial and three real benchmark data sets were employed.


Local Model Radial Basis Function Breast Cancer Dataset Nonlinear Activation Function Input Data Point 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yuridia Gago-Pallares
    • 1
  • Oscar Fontenla-Romero
    • 1
  • Amparo Alonso-Betanzos
    • 1
  1. 1.University of A Coruña, Department of Computer Science, 15071 A CoruñaSpain

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