Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Creation of Classifier Ensembles for Handwritten Word Recognition Using Feature Selection Algorithms
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Introducing a very large dataset of handwritten Farsi digits and a study on their varieties
Pattern Recognition Letters
Learn to Detect Phishing Scams Using Learning and Ensemble ?Methods
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
A New Approach to Improve the Vote-Based Classifier Selection
NCM '08 Proceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management - Volume 02
NCM '08 Proceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management - Volume 02
Using genetic algorithms for data mining optimization in an educational web-based system
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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This paper proposes a heuristic classifier ensemble to improve the performance of learning in multiclass problems. Although the more accurate classifier leads to a better performance, there is another approach to use many inaccurate classifiers while each one is specialized for a few data in the problem space and using their consensus vote as the classifier. In this paper, some ensembles of classifiers are first created. The classifiers of each of these ensembles jointly work using majority weighting votes. The results of these ensembles are combined to decide the final vote in a weighted manner. Finally the outputs of these ensembles are heuristically aggregated. The proposed framework is evaluated on a very large scale Persian digit handwritten dataset and the experimental results show the effectiveness of the algorithm.