New dynamic classifiers selection approach for handwritten recognition

Authors:
Nabiha Azizi;Nadir Farah;Abdel Ennaji
Affiliations:
Labged Laboratory: Laboratoire de Gestion électronique de documents, Departement d’informatique, Université Badji Mokhtar, Annaba, Algeria;Labged Laboratory: Laboratoire de Gestion électronique de documents, Departement d’informatique, Université Badji Mokhtar, Annaba, Algeria;Litis Laboratory, Laboratoire d’Informatique, du Traitement de l’Information et des Systèmes (LITIS), Rouen University, France
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Year:
2012

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Abstract

In this paper a new approach based on dynamic selection of ensembles of classifiers is discussed to improve handwritten recognition system. For pattern classification, dynamic ensemble learning methods explore the use of different classifiers for different samples, therefore, may get better generalization ability than static ensemble learning methods. Our proposed DECS-LR algorithm (Dynamic Ensemble of Classifiers Selection by Local Reliability) enriched the selection criterion by incorporating a new Local-Reliability measure and chooses the most confident ensemble of classifiers to label each test sample dynamically. Confidence level is estimated by proposed reliability measure using confusion matrix constructed during training level. After validation with voting and weighted voting fusion methods, ten different classifiers and three benchmarks, we show experimentally that choosing classifiers ensemble dynamically taking into account the proposed L-Reliability measure leads to increase recognition rate for Handwritten recognition system using three benchmarks.