Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Pattern classification with genetic algorithms
Pattern Recognition Letters - Special issue on genetic algorithms
Machine Learning
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
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
Using learning to facilitate the evolution of features for recognizing visual concepts
Evolutionary Computation
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
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 an innovative combinational algorithm to improve the performance of multiclass problems. Because the more accurate classifier the better performance of classification, so researchers have been tended to improve the accuracies of classifiers. Although obtaining the more accurate classifier is often targeted, there is an alternative way to reach for it. Indeed one can use many inaccurate classifiers each of which is specialized for a few dataitems in the problem space and then s/he can consider their consensus vote as the classification. This paper proposes a new ensembles methodology that uses ensemble of classifiers as elements of ensemble. These ensembles of classifiers jointly work using majority weighted voting. The results of these ensembles are in weighted manner combined to decide the final vote of the classification. In empirical result, these weights in final classifier are determined with using a series of genetic algorithms. We evaluate the proposed framework on a very large scale Persian digit handwritten dataset and the results show effectiveness of the algorithm.