Abstract
The performance of neural nets can be improved through the use of ensembles of redundant nets. In this paper, some of the available methods of ensemble creation are reviewed and the “test and select” methodolology for ensemble creation is considered. This approach involves testing potential ensemble combinations on a validation set, and selecting the best performing ensemble on this basis, which is then tested on a final test set. The application of this methodology, and of ensembles in general, is explored further in two case studies. The first case study is of fault diagnosis in a diesel engine, and relies on ensembles of nets trained from three different data sources. The second case study is of robot localisation, using an evidence-shifting method based on the output of trained SOMs. In both studies, improved results are obtained as a result of combining nets to form ensembles.
We would like to thank the EPSRC Grant No.GR/K84257 for funding this research.
G.O.Chandroth is now at Lloyds Register, but his contribution to this paper was made whilst he was at University of Sheffield
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Sharkey, A.J.C. (1999) Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems. London, Springer-Verlag.
Freund, Y and Schapire, R. (1996) Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning, pp149–156, Morgan Kaufmann.
Jacobs, R.A. (1995) Methods for combining experts’ probability assessments. Neural Computation, 7, 867–888.
Sharkey, A.J.C., Chandroth, G.O, and Sharkey, N.E. (in press) A Multi-Net System for the Fault Diagnosis of a Diesel Engine. Neural Computing and Applications.
Sharkey, A.J.C., Sharkey, N.E., and Cross, S.S. (1998) Adapting an Ensemble Approach for the Diagnosis of Breast Cancer. In Proceedings of ICANN 98, Springer-Verlag, pp 281–286.
Sharkey, A.J.C. (1999) Multi-net Systems. In (Ed) Amanda Sharkey Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems. London, Springer-Verlag, pp1–25.
Sharkey, A.J.C. (1996) On combining artificial neural nets. Connection Science: Special Issue on Combining Artificial Neural Nets, Ensemble Approaches, 8(3/4) 299–314.
Dietterich, T.G. (1997) Machine learning research: Four current directions. AI Magazine, 18(4), 97–136.
Perrone, M.P. and Cooper, L.N. (1993) When networks disagree: Ensemble methods for hybrid neural networks. In R.J. Mammone, editor, Neural Networks for Speech and Image Processing, Chapter 10, Chapman-Hall.
Partridge, D., and Yates, W.B. (1996) Engineering multiversion neural-net systems. Neural Computation, 8(4), 869–893.
Breiman, L. (1996) Bagging Predictors. Machine Learning, 24, 123–140.
Sharkey, A.J.C., Sharkey, N.E. and Chandroth, G.O. (1996) Diverse Neural Net solutions to a Fault Diagnosis Problem. Neural Computing and Applications, 4, 218–227.
Parmanto, B., Munro, P.W. and Doyle, H.R. (1996) Reducing variance of committee prediction with resampling techniques. Connection Science, 8,3/4, 405–416.
Sharkey, A.J.C., Chandroth, G.O. and Sharkey, N.E. (2000) Acoustic Emission, Cylinder Pressure and Vibration: A Multisensor Approach to Robust Fault Diagnosis. In Proceedings of IJCNN2000, Como, Italy, July.
Chandroth, G.O, Sharkey, A.J.C., and Sharkey, N.E. (1999) Vibration signatures, wavelets and principal components in diesel engine diagnostics. In Proceedings of ODRA’ 99: Third International Conference on Marine Technology, 1999.
Chandroth, G.O. (2000) Diagnositic classifier ensembles: enforcing diversity for reliability in the combination. PhD Dissertation, University of Sheffield.
Tumer, K. and Ghosh, J. (1996) Error correlation and error reduction in ensemble classifiers. Connection Science, 8,3/4, 385–404.
Dietterich, T.G. and Bakiri, G. (1995) Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2, 263–286.
Breiman, L. (1998) Randomising outputs to increase prediction accuracy, Technical Report 518, Statistic Department, University of California.
Raviv, Y. and Intrator, N. (1996) Bootstrapping with noise: an effective regularization technique. Connection Science, 8,3/4, 355–372.
Sharkey, A.J.C. and Sharkey, N.E. (1997) Combining Diverse Neural Nets, Knowledge Engineering Review, 12,3, 1–17.
Parmanto, B., Munro, P.W. and Doyle, H.R. (1994) Neural Network classifier for hepatoma detection. In Proceedings of the World Congress on Neural Networks, Vol 1, Mahway, NJ. Lawrence Erlbaum Associates.
Breiman, L. (1999) Combining predictors. In A.J.C. Sharkey (Ed) Combining Artificial Neural Nets: Ensemble and Modular Multi-net Systems. London, Springer-Verlag, pp 31–50.
Schapire, R., Freund, Y., Bartlett, P. and Lee, W. (1997) Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5): 1651–1686.
Quinlan, J.R. (1996) Bagging, boosting and C4.5. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp725–730 Cambridge, MA. AAAI Press/MIT Press.
Krogh, A., and Vedelsby, J. (1995) Neural network ensembles, cross validation, and active learning. In G. Tesauro, D. Touretzky, and T. Leen (Eds) Advances in Neural Information Processing Systems, 7, 231–238, Cambridge, MA, MIT Press.
Geman, S., Bienenstock, E., Doursat, R. (1992) Neural networks and the bias/variance dilemma. Neural Computation, 4, pp1–58.
Opitz, D.W. and Shavlik, J.W. (1996) Actively searching for an effective neural network ensemble. Connection Science, 8,3/4, 337–353.
Hashem, S. (1996) Effects of collinearity on combining neural networks. Connection Science, 8,3/4, 315–336.
Gerecke, U., and Sharkey, N.E. (1999) Quick and dirty localisation for a lost robot. In Proceedings of IEEE international conference on Computational Intelligence for Robotics and Automation (CIRA-99), Monterey, CA.
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Sharkey, A.J.C., Sharkey, N.E., Gerecke, U., Chandroth, G.O. (2000). The “Test and Select” Approach to Ensemble Combination. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, vol 1857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45014-9_3
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DOI: https://doi.org/10.1007/3-540-45014-9_3
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