Abstract
The Multilayer Perceptrons (MLPs) are the most popular class of Neural Networks. When applying MLPs, the search for the ideal architecture is a crucial task, since it should should be complex enough to learn the input/output mapping, without overfitting the training data. Under this context, the use of Evolutionary Computation makes a promising global search approach for model selection. On the other hand, ensembles (combinations of models) have been boosting the performance of several Machine Learning (ML) algorithms. In this work, a novel evolutionary technique for MLP design is presented, being also used an ensemble based approach. A set of real world classification and regression tasks was used to test this strategy, comparing it with a heuristic model selection, as well as with other ML algorithms. The results favour the evolutionary MLP ensemble method.
This work was supported by the FCT project POSI/ROBO/43904/2002, which is partially funded by FEDER.
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References
Haykin, S. (1999) Neural Networks-A Compreen-sive Foundation. 2nd ed. Prentice-Hall
Quinlan, J.R. (1994) Comparing Connectionist and Symbolic Learning Methods. In: Hanson, S. et al. (eds.) Computation Learning: Theory and Natural Learning Systems, MIT Press, pp. 445–456
Setiono, R. (2003) Techniques for Extracting Classification and Regression Rules from Artificial Neural Networks. In D. Fogel and C. Robinson, (eds.), Computational Intelligence: The Experts Speak, IEEE Press/Wiley, pp 99–114
Thimm, G. and Fiesler, E. (1995) Evaluating pruning methods. In: Proc. of the Int. Symp. on Artificial Neural Networks, pp 20–25
Kwok, T. and Yeung, D. (1997) Constructive algorithms for structure learning in feedforward neural networks for regression problems problems: A survey. IEEE Transactions on Neural Networks, 8(3):630–645
Yao, X. (1999) Evolving Artificial Neural Networks. In: Proc. of the IEEE, 87(9): 1423–1447
Dietterich, T. (1997) Machine Learning Research: Four Current Directions. AI Magazine, 18(4):97–136
Rocha, M., Cortez, P. and Neves, J. Ensembles of Artificial Neural Networks with Heterogeneous Topologies. In: Proc. of the 4th Symposium on Engineering of Intelligent Systems (EIS2004). ICSC Academic Press
Blake, C. and Merz, C. (1998) UCI Repository of Machine Learning Databases, University of California
Cortez, P., Rocha, M. and Neves, J. (2001) Evolving Time Series Forecasting Neural Network Models. In: Proc. of the 3rd Int. Symposium on Adaptive Systems: Evolutionary Computation and Probabilistic Graphical Models (ISAS 2001), pp. 84–91
Riedmiller, M. (1994) Supervised Learning in Multilayer Perceptrons-from Backpropagation to Adaptive Learning Techniques. Computer Standards and Interfaces 16
Witten, I. and Frank, E. (2000) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann
Kohavi, R. (1995) A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proc. of the Int. Joint Conference on Artificial Intelligence (IJCAI)
Liu, Y., Yao, X. and Higuchi, T. (2000) Evolutionary Ensembles with Negative Correlation Learning. IEEE Transactions on Evolutionary Computation, 4(4):380–387
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Rocha, M., Cortez, P., Neves, J. (2005). Evolutionary Design of Neural Networks for Classification and Regression. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds) Adaptive and Natural Computing Algorithms. Springer, Vienna. https://doi.org/10.1007/3-211-27389-1_73
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DOI: https://doi.org/10.1007/3-211-27389-1_73
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