Archivio Istituzionale della RicercaIn this paper we solve the problem of classifying chestnut plants according to their place of origin. We compare the results obtained by state of the art classifiers, among which, MLP, RBF, SVM, C4.5 decision tree and random forest. We determine which features are meaningful for the classification, the achievable classification accuracy of these classifiers families with the available features and how much the classifiers are robust to noise. Among the obtained classifiers, neural networks show the greatest robustness to noise.
Classification of chestnuts with feature selection by noise resilient classifiers / Roglia, E.; Cancelliere, R.; Meo, R.. - (2008), pp. 271-276. (Intervento presentato al convegno 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2008 tenutosi a Bruges, bel nel 2008).
Classification of chestnuts with feature selection by noise resilient classifiers
Roglia E.;Meo R.
2008
Abstract
In this paper we solve the problem of classifying chestnut plants according to their place of origin. We compare the results obtained by state of the art classifiers, among which, MLP, RBF, SVM, C4.5 decision tree and random forest. We determine which features are meaningful for the classification, the achievable classification accuracy of these classifiers families with the available features and how much the classifiers are robust to noise. Among the obtained classifiers, neural networks show the greatest robustness to noise.
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https://hdl.handle.net/11583/2942352