Abstract
Machine ensembles are learning architectures that offer high expressive capacities and, consequently, remarkable performances. This is due to their high number of trainable parameters.
In this paper, we explore and discuss whether binarization techniques are effective to improve standard diversification methods and if a simple additional trick, consisting in weighting the training examples, allows to obtain better results. Experimental results, for three selected classification problems, show that binarization permits that standard direct diversification methods (bagging, in particular) achieve better results, obtaining even more significant performance improvements when pre-emphasizing the training samples. Some research avenues that this finding opens are mentioned in the conclusions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Sig. Syst. 2, 303–314 (1989)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359–366 (1989)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000). doi:10.1007/3-540-45014-9_1
Breiman, L.: Bagging predictors. Mach. Learn. 4, 123–140 (1996)
Hinton, G.E., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief networks. Neural Comput. 18, 1527–1554 (2006)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)
Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37, 297–336 (1999)
Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixture of local experts. Neural Comput. 3, 79–87 (1991)
Omari, A., Figueiras-Vidal, A.R.: Feature combiners with gate-generated weights for classification. IEEE Trans. Neural Netw. Learn. Syst. 24, 158–163 (2013)
Leisch, F., Hornik, K.: Combining neural network voting classifiers and error correcting output codes. In: MEASURMENT 1997 (1997)
Cemre, Z., Windeatt, T., Yanikogl, B.: Bias-variance analysis of ECOC and bagging using neural nets. In: Okun, O., Valentini, G., Re, M. (eds.) Ensembles in Machine Learning Applications. Studies in Computational Intelligence, vol. 373, pp. 59–73. Springer, Heidelberg (2011)
Gómez-Verdejo, V., Ortega-Moral, M., Arenas-García, J., Figueiras-Vidal, A.R.: Boosting by weighting critical and erroneous samples. Neurocomput. 69, 679–685 (2006)
Gómez-Verdejo, V., Arenas-García, J., Figueiras-Vidal, A.R.: A dynamically adjusted mixed emphasis method for building boosting ensembles. IEEE Trans. Neural Netw. 19, 3–17 (2008)
Alvear-Sandoval, R.F., Figueiras-Vidal, A.R.: An experiment in pre-emphasizing diversified deep neural networks. In: 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 527–532 (2016)
Rokach, L.: Pattern Classification Using Ensemble Methods. World Scientific, Singapore (2010)
Dietterich, T.G., Bakiri, G.: Solving multi-class learning problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263–286 (1995)
Vurkaç, M.: Clave-direction analysis: a new arena for educational and creative of music technology. J. Music Technol. Educ. 4, 27–46 (2011)
Giannakopoulos, X., Karhunen, J., Oja, E.: An experimental comparison of neural algorithms for independent component analysis and blind separation. Int. J. Neural Syst. 9, 99–114 (1999)
Siebert, J.P.: Vehicle Recognition Using Rule Based Methods. Turing Institute Research Memorandum TIRM-87-018, Glasgow, Scotland (1987)
Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2013)
Acknowledgments
This work has been partly supported by research grants CASI-CAM-CM (S2013/ICE-2845, DGUI-CM and FEDER) and Macro-ADOBE (TEC2015-67719-P, MINECO).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Álvarez-Pérez, L., Ahachad, A., Figueiras-Vidal, A.R. (2017). Pre-emphasizing Binarized Ensembles to Improve Classification Performance. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-59153-7_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59152-0
Online ISBN: 978-3-319-59153-7
eBook Packages: Computer ScienceComputer Science (R0)