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Author: Philippe Thomas

Affiliation: Université de Lorraine and CNRS, France

ISBN: 978-989-758-157-1

Keyword(s): Cost-Sensitive Approach, Multilayer Perceptron, Outliers, Robustness, Levenberg-Marquardt, Label Noise.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: In learning approaches for classification problem, the misclassification error types may have different impacts. To take into account this notion of misclassification cost, cost sensitive learning algorithms have been proposed, in particular for the learning of multilayer perceptron. Moreover, data are often corrupted with outliers and in particular with label noise. To respond to this problem, robust criteria have been proposed to reduce the impact of these outliers on the accuracy of the classifier. This paper proposes to associate a cost sensitivity weight to a robust learning rule in order to take into account simultaneously these two problems. The proposed learning rule is tested and compared on a simulation example. The impact of the presence or absence of outliers is investigated. The influence of the costs is also studied. The results show that the using of conjoint cost sensitivity weight and robust criterion allows to improve the classifier accuracy.

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Paper citation in several formats:
Thomas, P. (2015). Perceptron Learning for Classification Problems - Impact of Cost-Sensitivity and Outliers Robustness.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 106-113. DOI: 10.5220/0005594001060113

@conference{ncta15,
author={Philippe Thomas.},
title={Perceptron Learning for Classification Problems - Impact of Cost-Sensitivity and Outliers Robustness},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)},
year={2015},
pages={106-113},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005594001060113},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)
TI - Perceptron Learning for Classification Problems - Impact of Cost-Sensitivity and Outliers Robustness
SN - 978-989-758-157-1
AU - Thomas, P.
PY - 2015
SP - 106
EP - 113
DO - 10.5220/0005594001060113

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