Abstract
The common practices of machine learning appear to be frustrated by a number of theoretical results denying the possibility of any meaningful implementation of a “superior” learning algorithm. However, there exist some general assumptions that, even when overlooked, preside the activity of researchers and practitioners. A thorough reflection over such essential premises brings forward the meta-learning approach as the most suitable for escaping the long-dated riddle of induction claiming also an epistemologic soundness. Several examples of meta-learning models can be found in literature, yet the combination of computational intelligence techniques with meta-learning models still remains scarcely explored. Our contribution to this particular research line consists in the realisation of Mindful, a meta-learning system based on the neuro-fuzzy hybridisation. We present the Mindful system firstly situating it inside the general context of the meta-learning frameworks proposed in literature. Finally, a complete session of experiments is illustrated, comprising both base-level and meta-level learning activity. The appreciable experimental results underline the suitability of the Mindful system for managing past accumulated learning experience while facing novel tasks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aha, D.W.: Generalizing from case studies: a case study. In: Proceedings of the Ninth International Conference on Machine Learning, MLC 1992 (1992)
Brodley, C.: Addressing the selective superiority problem: automatic algorithm/model class selection. In: Proceedings of the Tenth International Conference on Machine Learning (MLC 1993), pp. 17–24 (1993)
Desjardins, M., Gordon, D.: Evaluation and selection of bias in machine learning. Machine Learning 20, 5–22 (1995)
Giraud-Carrier, C., Vilalta, R., Brazdil, P.: Introduction to the special issue on meta-learning. Machine Learning 54, 187–193 (2004)
Vilalta, R., Drissi, Y.: A perspective view and survey of Meta-Learning. Artificial Intelligence Review 18, 77–95 (2002)
Vilalta, R., Giraud-Carrier, C., Brazdil, P.: Meta-Learning: Concepts and Techniques. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Springer, Heidelberg (2005)
Hume, D.: A Treatise of Human Nature (1740)
Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)
Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Search. Technical Report, Santa Fe Institute (1995)
Schaffer, C.: A conservation law for generalization performance. In: Proceedings of the Eleventh International Conference on Machine Learning (ICML 1994), pp. 259–265 (1994)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, Chichester (2001)
Giraud-Carrier, C., Provost, F.: Toward a Justification of Meta-learning: Is the No Free Lunch Theorem a Show-stopper? In: Proceedings of the ICML Workshop on Meta-Learning, pp. 9–16 (2005)
Bensusan, H., Giraud-Carrier, C.: Discovering Task Neighbourhoods through Landmark Learning Performances. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 325–330. Springer, Heidelberg (2000)
Chan, P.K., Stolfo, S.J.: Experiments on multistrategy learning by meta-learning. In: Proc. Second International Conference Information and Knowledge Management, pp. 314–323 (1993)
Domingos, P.: Knowledge Discovery Via Multiple Models. Intelligent Data Analysis 2, 187–202 (1998)
Kalousis, A., Hilario, M.: Model Selection Via Meta-Learning: a Comparative Study. In: Proceedings of the 12th International IEEE Conference on Tools with AI. IEEE Press, Los Alamitos (2000)
Schweighhofer, N., Doya, K.: Meta-Learning in Reinforcement Learning. Neural Networks 16, 5–9 (2003)
van Someren, M.: Model class selection and construction: Beyond the procrustean approach to machine learning applications. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), vol. 2049, pp. 196–217. Springer, Heidelberg (2001)
Vilalta, R., Giraud-Carrier CBrazdil, P., Soares, C.: Using Meta-Learning to Support Data Mining. International Journal of Computer Science and Applications 1, 31–45 (2004)
Bensusan, H.N.: Automatic Bias Learning: an inquiry into the inductive basis of induction. Ph.D. thesis,school University of Sussex (1999)
Rice, J.: The algorithm selection problem. Advances in Computers 15, 65–118 (1976)
Smith-Miles, K.A.: Cross-disciplinary Perspectives on Meta-learning for Algorithm Selection. ACM Computing Surveys (2009)
Asuncion, A., Newman, D.: UCI Machine Learning Repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Silver, D.L.: Selective Transfer of Neural Network task knowledge, Ph.D. thesis,School University of Western Ontarioaddress London, Ontario (2000)
Thrun, S.: Is learning the n-th thing any easier than learning the first? In: Touretzky, D., Mozer, M., Hasselmo, M.E. (eds.) Advances in Neural Information Processing Systems, pp. 640–646. MIT Press, Cambridge (1996)
Thrun, S.: Lifelong learning algorithms. In: Thrun, S., Pratt, L. (eds.) Learning to learn, pp. 181–209. Kluwer Academic Publishers, Dordrecht (1998)
Castiello, C.: Meta-learning: a concern for epistemology and computational intelligence, Ph.D. thesis, school University of Bari, Bari, Italy (2005)
Soares, C., Brazdil, P., Kuba, P.: A Meta-Learning Method to Select the Kernel Width in Support Vector Regression. Machine Learning 54, 195–209 (2004)
Michie, D., Spiegelhalter, D., Taylor, C.: Machine learning, neural and statistical classification. Ellis Horwood Series in Artificial Intelligence (1994)
Gama, J., Brazdil, P.: Characterization of classification algorithms. In: Pinto-Ferreira, C., Mamede, N.J. (eds.) EPIA 1995. LNCS, vol. 990, pp. 83–102. Springer, Heidelberg (1995)
Linder, C., Studer, R.: AST: Support for Algorithm Selection with a CBR Approach. In: Recent Advances in Meta-Learning and Future Work, pp. 418–423 (1999)
Sohn, S.Y.: Meta analysis of classification algorithms for pattern recognition. JournalIEEE Transactions on Pattern Analysis and Machine Intelligence 21, 1137–1144 (1999)
Castiello, C., Castellano, G., Fanelli, A.M.: Meta-data: Characterization of input features for meta-learning. In: Torra, V., Narukawa, Y., Miyamoto, S. (eds.) MDAI 2005. LNCS (LNAI), vol. 3558, pp. 457–468. Springer, Heidelberg (2005)
Bensusan, H.: Odd Bites into Bananas Don’t Make You Blind: Learning about Simplicity and Attribute Addition. In: Proceedings of the ECML Workshop on Upgrading Learning to the Meta-level: Model Selection and Data Transformation, pp. 30–42 (1998)
Bensusan, H., Giraud-Carrier, C., Kennedy, C.: A Higher order Approach to Meta-learning. In: Proceedings of the ECML Workshop on Meta-learning: Building Automatic Advice Strategies for Model Selection and Method Combination, pp. 109–118 (2000)
Peng, Y., Flach, P., Brazdil, P., Soares, C.: Improved Data Set Characterisation for Meta-learning. In: Proceedings of the Fifth International Conference on Discovery Science, pp. 141–152 (2002)
Pfahringer, B., Bensusan, H., Giraud-Carrier, C.: Tell me who can learn and I can tell who you are: landmarking various learning algorithms. In: Langley, P. (ed.) Proceeding of the 17th International Conference on Machine Learning (ICML2000), pp. 743–750. Morgan Kaufman, San Francisco (2000)
Brodley, C.: Recursive automatic bias selection for classifier construction. Machine Learning 20, 63–94 (1995)
Berrer, H., Paterson, I., Keller, J.: Evaluation of Machine learning Algorithm Ranking Advisors. In: Proceedings of the PKDD Workshop on Data-Mining, Decision Support, Meta-Learning and ILP: Forum for Practical Problem Presentation and Prospective Solutions, pp. 1–13 (2000)
Soares, C., Brazdil, P.B.: Zoomed ranking: Selection of classification algorithms based on relevant performance information. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 126–135. Springer, Heidelberg (2000)
Brazdil, P., Soares, C., Pinto, J.: Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results. Machine Learning 50, 251–277 (2003)
Giraud-Carrier, C.: Beyond Predictive Accuracy: What? In: Proceedings of the EMCL 1998 Workshop on Upgrading Learning to Meta-Learning: Model Selection and Data Transformation, pp. 78–85 (1998)
Abraham, A.: Meta-learning evolutionary artificial neural networks. Neurocomputing Journal 56, 1–38 (2004)
Castiello, C., Fanelli, A.M.: Hybrid strategies and meta-learning: an inquiry into the epistemology of artificial learning. Research on Computing Science 16, 153–162 (2005)
Castiello, C.: Meta-Learning and Neurocomputing A New Perspective for Computational Intelligence. In: Hassanien, A.E., Abraham, A., Vasilakos, A., Pedrycz, W. (eds.) Foundations of Computational Intelligence, vol. 1, Springer, Heidelberg (2009)
Zadeh, L.A.: Fuzzy Sets. Infom. and Contr. 8, 338–353 (1965)
Zadeh, L.A., Kacprzyk, J.: Computing with Words in Information. Physica-Verlag, Heidelberg (1999)
Jang, J.S.R., Sun, C.T.: Neuro-Fuzzy Modeling and Control. Proceedings of the IEEE 83, 378–406 (1995)
Kosko, B.: Neural Networks and Fuzzy Systems: a Dynamical Systems Approach. Prentice Hall, Englewood Cliffs (1991)
Lin, C.T., Lee, C.S.G.: Neural Fuzzy System: a Neural-Fuzzy Synergism to Intelligent Systems. Prentice-Hall, Englewood Cliffs (1996)
Jang, J.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. System, Man and Cybernetics 23, 665–685 (1993)
Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets and Systems 28, 15–33 (1988)
Castellano, G., Castiello, C., Fanelli, A.M., Mencar, C.: Knowledge Discovery by a Neuro-Fuzzy Modeling Framework. Fuzzy Sets and Systems 149, 187–207 (2005)
Castiello, C., Castellano, G., Fanelli, A.M.: MINDFUL: a framework for Meta-INDuctive neuro-FUzzy Learning. Information Sciences 178, 3253–3274 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Castiello, C., Fanelli, A.M. (2011). Computational Intelligence for Meta-Learning: A Promising Avenue of Research. In: Jankowski, N., Duch, W., Gra̧bczewski, K. (eds) Meta-Learning in Computational Intelligence. Studies in Computational Intelligence, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20980-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-20980-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20979-6
Online ISBN: 978-3-642-20980-2
eBook Packages: EngineeringEngineering (R0)