Result: A neural adaptive algorithm for feature selection and classification of high dimensionality data

Title:
A neural adaptive algorithm for feature selection and classification of high dimensionality data
Authors:
BINAGHI, Elisabetta, GALLO, Ignazio, BOSCHETTI, Mirco, BRIVIO, P. Alessandro
Source:
Image analysis and processing (Cagliari, 6-8 September 2005)Lecture notes in computer science. :753-760
Publisher Information:
Berlin: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 12 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Algorithme adaptatif, Adaptive algorithm, Algoritmo adaptativo, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Analyse statistique, Statistical analysis, Análisis estadístico, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Capteur imagerie hyperspectral, Hyperspectral imaging sensor, Sensor hiperespectral de formación de imágenes, Classification, Clasificación, Dimensionnalité, Dimensionality, Dimensionalidad, Machine exemple support, Vector support machine, Máquina ejemplo soporte, Méthode adaptative, Adaptive method, Método adaptativo, Perceptron multicouche, Multilayer perceptrons, Réseau multicouche, Multilayer network, Red multinivel, Réseau neuronal, Neural network, Red neuronal, Topologie, Topology, Topología, Télédétection, Remote sensing, Teledetección
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Dipartimento di Informatica e Comunicazione, Universita' degli Studi dell'Insubria, Varese, Italy
CNR-IREA, Institute for Electromagnetic Sensing of the Environment, Via Bassini 15, 20133 Milan, Italy
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17135397
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.17135397
Database:
PASCAL Archive

Further Information

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 Multi-Layer 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.