Abstract
This paper discusses possibilities how to use input filters to improve performance in ensemble of neural-networks-based classifiers. The proposed method is based on filtering of input vectors in the used training set, which minimize demands on data preprocessing. Our approach comes out from a technique called boosting, which is based on the principle of combining a large number of so-called weak classifiers into a strong classifier. In the experimental study, we verified that such classifiers are able to sufficiently classify the submitted data into predefined classes without knowledge of details of their significance.
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Acknowledgments
The research described here has been financially supported by University of Ostrava grant SGS17/PRF/2015. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.
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Volna, E., Kotyrba, M., Kocian, V. (2015). Input Filters Implementing Diversity in Ensemble of Neural Networks. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_26
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DOI: https://doi.org/10.1007/978-3-319-19644-2_26
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