Classification of chestnuts with feature selection by noise resilient classifiers

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.

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Elena Roglia, Rossella Cancelliere, Rosa Meo
• Keywords: Achievable Classification Accuracy, Art Classifiers, Classifiers Families, ESANN 2008, Neural Networks