2005 | OriginalPaper | Buchkapitel
A Neural Adaptive Algorithm for Feature Selection and Classification of High Dimensionality Data
verfasst von : Elisabetta Binaghi, Ignazio Gallo, Mirco Boschetti, P. Alessandro Brivio
Erschienen in: Image Analysis and Processing – ICIAP 2005
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.