Abstract
In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise the ensemble. There exists a tradeoff as to what should be the optimal measures of diversity and accuracy. The aim of this paper is to address this issue. We propose the DIVACE algorithm which tries to produce an ensemble as it searches for the optimum point on the diversity-accuracy curve. The DIVACE algorithm formulates the ensemble learning problem as a multi-objective problem explicitly.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Abbass, H.A.: A memetic pareto evolutionary approach to artificial neural networks. In: Proceedings of the 14th Australian Joint Conference on Artificial Intelligence, Berlin, pp. 1–12. Springer, Heidelberg (2000)
Abbass, H.A.: Pareto neuro-evolution: Constructing ensemble of neural networks using multi-objective optimization. In: The IEEE 2003 Conference on Evolutionary Computation, vol. 3, pp. 2074–2080. IEEE Press, Los Alamitos (2003)
Abbass, H.A.: Pareto neuro-ensemble. In: Gedeon, T(T.) D., Fung, L.C.C. (eds.) AI 2003. LNCS (LNAI), vol. 2903, pp. 554–566. Springer, Heidelberg (2003a)
Abbass, H.A., Sarker, R., Newton, C.: PDE: A pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2001), vol. 2, pp. 971–978. IEEE Press, Los Alamitos (2001)
Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: A survey and categorisation. Journal of Information Fusion (2004) (to appear)
Geman, S., Bienenstock, E., Doursat, R.: Neural networks and the bias/variance dilemma. Neural Computation 4(1), 1–58 (1992)
Liu, Y., Yao, X., Higuchi, T.: Evolutionary ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation 4(4), 380–387 (2000)
Liu, Y., Yao, X.: Ensemble learning via negative correlation. Neural Networks 12(10), 1399–1404 (1999)
Liu, Y., Yao, X.: Learning and evolution by minimization of mutual information. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 495–504. Springer, Heidelberg (2002)
Srinivas, N., Deb, K.: Multi-objective function optimization using nondominated sorting genetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)
Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley, USA (1995)
Yao, X., Liu, Y.: Evolving neural network ensembles by minimization of mutual information. International Journal of Hybrid Intelligent Systems 1(1) (January 2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chandra, A., Yao, X. (2004). DIVACE: Diverse and Accurate Ensemble Learning Algorithm. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_91
Download citation
DOI: https://doi.org/10.1007/978-3-540-28651-6_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
Online ISBN: 978-3-540-28651-6
eBook Packages: Springer Book Archive