Abstract
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.
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Parvin, H., Minaei, B., Alizadeh, H. (2011). A Heuristic Classifier Ensemble for Huge Datasets. In: Zhong, N., Callaghan, V., Ghorbani, A.A., Hu, B. (eds) Active Media Technology. AMT 2011. Lecture Notes in Computer Science, vol 6890. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23620-4_7
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DOI: https://doi.org/10.1007/978-3-642-23620-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23619-8
Online ISBN: 978-3-642-23620-4
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