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A Neural Adaptive Algorithm for Feature Selection and Classification of High Dimensionality Data

  • Elisabetta Binaghi
  • Ignazio Gallo
  • Mirco Boschetti
  • P. Alessandro Brivio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

In this paper, we propose a novel method which involves neural adaptive techniques for identifying salient features and for classifying high dimensionality data. In particular a network pruning algorithm acting on MultiLayer Perceptron topology is the foundation of the feature selection strategy. Feature selection is implemented within the back-propagation learning process and based on a measure of saliency derived from bell functions positioned between input and hidden layers and adaptively varied in shape and position during learning. Performances were evaluated experimentally within a Remote Sensing study, aimed to classify hyperspectral data. A comparison analysis was conducted with Support Vector Machine and conventional statistical and neural techniques. As seen in the experimental context, the adaptive neural classifier showed a competitive behavior with respect to the other classifiers considered; it performed a selection of the most relevant features and showed a robust behavior operating under minimal training and noisy situations.

Keywords

Feature Selection Hide Neuron High Dimensionality Data Adaptive Model Spectral Angle Mapper 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Elisabetta Binaghi
    • 1
  • Ignazio Gallo
    • 1
  • Mirco Boschetti
    • 2
  • P. Alessandro Brivio
    • 2
  1. 1.Dipartimento di Informatica e ComunicazioneUniversita’ degli Studi, dell’InsubriaVareseItaly
  2. 2.Institute for Electromagnetic Sensing of the EnvironmentCNR-IREAMilanItaly

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