Abstract
Building ensembles with good performance depends highly on the precision and on the diversity of the base learners that compose them. However, achieving base learners that are both precise and diverse is a complex issue. In this paper we explore the idea of resolving multiclass classification problems using base learners composed of coupled classifiers that are trained with disjoint datasets. The goal is to achieve an accurate ensemble by using base learners that are relatively accurate but highly diverse. The system resulting from this proposal has been validated on the MNIST dataset, which is a good example for multiclass problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience (2005)
Chandra, A., Chen, H., Yao, X.: Trade-Off Between Diversity and Accuracy in Ensemble Generation. Multi-objective Mach. Learn. Stud. Comput. Intell. 16, 429–464 (2006)
LeCun, Y.: THE MNIST DATABASE of handwritten digits, http://yann.lecun.com/exdb/mnist
Masulli, F., Valentini, G.: Comparing Decomposition Methods for Classification. In: Fourth International Conference on Knowledge-Based Intelligent Engineering. Systems and Allied Technologies, pp. 188–791 (2000)
Roli, F., Giacinto, G., Vernazza, G.: Methods for Designing Multiple Classifier Systems. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 78–87. Springer, Heidelberg (2001)
Sharkey, A.J.C., Sharkey, N.E., Gerecke, U., Chandroth, G.O.: The “Test and Select” Approach to Ensemble Combination. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 30–44. Springer, Heidelberg (2000)
Alonso-Weber, J.M., Sanchis, A.: A Skeletonizing Reconfigurable Self-Organizing Model: Validation Through Text Recognition. Neural Process. Lett. 34, 39–58 (2011)
Sesmero, M.P., Alonso-Weber, J.M., Gutiérrez, G., Ledezma, A., Sanchis, A.: A New Artificial Neural Network Ensemble based on Feature Selection and Class Recoding. Neural Comput. Appl. 21, 771–783 (2012)
Sesmero, M.P., Alonso-Weber, J.M., Gutiérrez, G., Ledezma, A., Sanchis, A.: Specialized Ensemble of Classifiers for Traffic Sign Recognition. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 733–740. Springer, Heidelberg (2007)
Sesmero, M.P., Alonso-Weber, J.M., Gutiérrez, G., Ledezma, A., Sanchis, A.: Ensemble of ANN for Traffic Sign Recognition. In: Rabuñal, Dorado, Pazos (eds.) Encyclopedia of Artificial Intelligence, pp. 554–560. IGI Global (2009)
Alonso-Weber, J.M., Sesmero, M.P., Gutierrez, G., Ledezma, A., Sanchis, A.: Handwritten Digit Recognition with Pattern Transformations and Neural Network Averaging. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds.) ICANN 2013. LNCS, vol. 8131, pp. 335–342. Springer, Heidelberg (2013)
Oza, N.C., Tumer, K.: Classifier Ensembles: Select Real-World Applications. Inf. Fusion 9, 4–20 (2008)
Sesmero, M.P.: Diseño. Análisis y Evaluación de Conjuntos de Clasificadores basados en Redes de Neuronas (2012), http://e-archivo.uc3m.es:8080/handle/10016/16177
Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. J. Mach. Learn. Res. 1, 113–141 (2000)
Breiman, L.: Bagging Predictors. Mach. Learn. 24, 123–140 (1996)
Duin, R.P.W., Tax, D.M.J.: Experiments with Classifier Combining Rules. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 16–29. Springer, Heidelberg (2000)
Dietterich, T.G., Bakiri, G.: Solving Multiclass Learning Problems via Error-Correcting Output Codes. J. Artif. Intell. Res. 2, 263–286 (1995)
Hall, M.A.: Correlation-based Feature Selection for Machine Learning (1999), http://www.cs.waikato.ac.nz/~mhall/thesis.pdf
Everitt, B.S.: The Analysis of Contingency Tables. Chapman and Hall, London (1977)
Dietterich, T.G.: Machine-Learning Research: Four Current Directions. AI Mag. 18, 97–137 (1997)
Kuncheva, L.I., Whitaker, C.J.: Ten Measures of Diversity in Classifier Ensem-bles: Limits for Two Classifiers. In: Proceedings of IEEE Workshop on Intelligent Sensor, pp. 10/1–10/6 (2001)
Tsymbal, A., Pechenizkiy, M., Cunningham, P.: Diversity in Search Strategies for Ensemble Feature Selection. Inf. Fusion 6, 83–98 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Sesmero, M.P., Alonso-Weber, J.M., Gutierrez, G., Sanchis, A. (2014). CCE: An Approach to Improve the Accuracy in Ensembles by Using Diverse Base Learners. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-07617-1_55
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07616-4
Online ISBN: 978-3-319-07617-1
eBook Packages: Computer ScienceComputer Science (R0)