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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References
Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)
Fausett, L.V.: Fundamentals of Neural Networks. Prentice-Hall Inc., Englewood Cliffs (1994)
Iwakura, T., Okamoto, S., Asakawa, K.: An adaboost using a weak-learner generating several weak hypotheses for large training data of natural language processing. IEEJ Trans. Electron. Inf. Syst. 130, 83–91 (2010)
Kocian, V., Volná, E.: Ensembles of neural-networks-based classifiers. In: Proceedings of the 18th International Conference on Soft Computing, Mendel 2012, Brno, pp. 256–261 (2012)
LeCun, Y., Cortes, C., Burges, C.: The MNIST Database. http://yann.lecun.com/exdb/mnist/. Accessed March 2014
Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33(1-2), 1–39 (2010)
Schapire, R.E.: A brief introduction to boosting. In: Proceedings of IJCAI 1999, pp. 1401–1406. Morgan Kaufmann Publishers Inc., San Francisco (1999)
Volna, E., Kocian, V., Kotyrba, M.: Boosting of neural networks over MNIST data. In: Proceedings of NCTA 2014, pp. 256–263. SCITEPRESS, Portugal (2014)
Wang, J., Yang, J., Li, S., Dai, Q., Xie, J.: Number image recognition based on neural network ensemble. In: Proceedings of the Third International Conference on Natural Computation, vol. 1. IEEE Computer Society (2007)
Yao, Y., Fu, Z., Zhao, X., Cheng, W.: Combining classifier based on decision tree. In: ICIE, 2009 WASE International Conference on Information Engineering, vol. 2, pp. 37–40 (2009)
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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