Abstract
This chapter describes a principled approach to meta-learning that has three distinctive features. First, whereas most previous work on meta-learning focused exclusively on the learning task, our approach applies meta-learning to the full knowledge discovery process and is thus more aptly referred to as meta-mining. Second, traditional meta-learning regards learning algorithms as black boxes and essentially correlates properties of their input (data) with the performance of their output (learned model). We propose to tear open the black box and analyse algorithms in terms of their core components, their underlying assumptions, the cost functions and optimization strategies they use, and the models and decision boundaries they generate. Third, to ground meta-mining on a declarative representation of the data mining (dm) process and its components, we built a DM ontology and knowledge base using the Web Ontology Language (owl).
The Data Mining Optimization Ontology (dmop, pronounced dee-mope)) provides a unified conceptual framework for analysing dm tasks, algorithms, models, datasets, workflows and performance metrics, as well as their relationships. The dm knowledge base uses concepts from dmop to describe existing data mining algorithms and their implementations in major dm software packages. Meta-data collected from data mining experiments are also described in terms of concepts from the ontology and linked to algorithm and operator descriptions in the knowledge base; they are then stored in data mining experiment data bases to serve as training and evaluation data for the meta-miner.
These three features together lay the groundwork for what we call deep or semantic meta-mining, i.e., dm process or workflow mining that is driven simultaneously by meta-data and by the collective expertise of data miners embodied in the data mining ontology and knowledge base. In Section 1, we review the state of the art in the fields of meta-learning and data mining ontologies; at the same time, we motivate the need for ontology-based meta-mining and distinguish our approach from related work in these two areas. Section 2 gives a detailed description of dmop, while Section 3 introduces a novel method for ontology-based discovery of generalized patterns from data mining workflows. Section 4 reports on proof-of-concept experiments conducted to gauge the efficacy of dmop-based workflow mining, and Section 5 concludes.
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References
Aha, D.W.: Lazy learning (editorial). Artificial Intelligence Review 11, 7–10 (1997)
Ali, S., Smith-Miles, K.: A meta-learning approach to automatic kernel selection for support vector machines. Neurocomputing 70(1-3), 173–186 (2006)
Anderson, M.L., Oates, T.: A review of recent research in metareasoning and metalearning. AI Magazine 28(1), 7–16 (2007)
Arimura, H.: Efficient algorithms for mining frequent and closed patterns from semi-structured data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 2–13. Springer, Heidelberg (2008)
Bartlett, P.: For valid generalization, the size of the weights is more important than the size of the network. In: Advances in Nueral Information Processing Systems, NIPS-1997 (1997)
Basu, M., Ho, T.K. (eds.): Data Complexity in Pattern Recognition. Springer, Heidelberg (2006)
Bensusan, H., Giraud-Carrier, C.: Discovering task neighbourhoods through landmark learning performances. In: Proceedings of the Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 325–330 (2000)
Bensusan, H., Giraud-Carrier, C., Kennedy, C.: A higher-order approach to meta-learning. In: Proceedings of the ECML 2000 workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination, June 2000, pp. 109–117 (2000)
Bernstein, A., Provost, F., Hill, S.: Toward intelligent assistance for a data mining process: An ontology-based approach for cost-sensitive classification. IEEE Transactions on Knowledge and Data Engineering 17(4), 503–518 (2005)
Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Blockeel, H., Vanschoren, J.: Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 6–17. Springer, Heidelberg (2007)
Brazdil, P., Gama, J., Henery, B.: Characterizing the applicability of classification algorithms using meta-level learning. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 83–102. Springer, Heidelberg (1994)
Brazdil, P., Giraud-Carrier, C., Soares, C., Vilalta, R. (eds.): Metalearning: Applications to Data Mining. Springer, Heidelberg (2009)
Brezany, P., Janciak, I., Min Tjoa, A.: Ontology-based construction of grid data mining workflows. In: Nigro, H.O., Gonzalez Cisaro, S.E., Xodo, D.H. (eds.) Data Mining with Ontologies: Implementations, Findings and Frameworks, IGI Global (2008)
Bringmann, B.: Matching in frequent tree discovery. In: Proc.4th IEEE International Conference on Data Mining (ICDM 2004), pp. 335–338 (2004)
Cacoveanu, S., Vidrighin, C., Potolea, R.: Evolutional meta-learning framework for automatic classifier selection. In: Proceedings of the IEEE 5th International Conference on Intelligent Computer Communication and Processing (ICCP 2009), pp. 27–30 (2009)
Cannataro, M., Comito, C.: A data mining ontology for grid programming. In: Proc. 1st Int. Workshop on Semantics in Peer-to-Peer and Grid Computing, in conjunction with WWW 2003, pp. 113–134 (2003)
Chapman, P., Clinton, J., Khabaza, T., Reinartz, T., Wirth, R.: The CRISP-DM process model. Technical report, CRISP-DM consortium (1999), http://www.crisp-dm.org
Cherkassky, V.: Model complexity control and statistical learning theory. Natural Computing 1, 109–133 (2002)
Diamantini, C., Potena, D., Storti, E.: Supporting users in KDD process design: A semantic similarity matching approach. In: Proc. 3rd Planning to Learn Workshop (held in conjunction with ECAI 2010), Lisbon, pp. 27–34 (2010)
DomingosA, P.: unified bias-variance decomposition for zero-one and squared loss. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence, pp. 564–569 (2000)
Duch, W., Grudzinski, K.: Meta-learning: Searching in the model space. In: Proc. of the Int. Conf. on Neural Information Processing (ICONIP), Shanghai 2001, pp. 235–240 (2001)
Duch, W., Grudziński, K.: Meta-learning via search combined with parameter optimization. In: Advances in Soft Computing, pp. 13–22. Springer, Heidelberg (2002)
Džeroski, S.: Towards a general framework for data mining. In: Džeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 259–300. Springer, Heidelberg (2007)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: An overview. In: Advances in Knowledge Discovery and Data Mining, pp. 1–34. MIT Press, Cambridge (1996)
Frank, A., Asuncion, A.: UCI machine learning repository (2010)
Fürnkranz, J., Petrak, J.: An evaluation of landmarking variants. In: Proceedings of the ECML Workshop on Integrating Aspects of Data Mining, Decision Support and Meta-learning, pp. 57–68 (2001)
Geman, S., Bienenstock, E., Doursat, R.: Neural networks and the bias/variance dilemma. Neural Computation 4, 1–58 (1992)
Giraud-Carrier, C., Vilalta, R., Brazdil, P.: Introduction to the special issue on meta-learning. Machine Learning 54, 187–193 (2004)
Gordon, D., DesJardins, M.: Evaluation and selection of biases in machine learning. Machine Learning 20, 5–22 (1995)
Grąbczewski, K., Jankowski, N.: Versatile and efficient meta-learning architecture: knowledge representation and management in computational intelligence. In: IEEE Symposium on Computational Intelligence and Data Mining, pp. 51–58 (2007)
Data Mining Group. Predictive Model Markup Language (PMML), http://www.dmg.org/
Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A. (eds.): Feature Extraction: Foundations and Applications. Springer, Heidelberg (2006)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46, 389–422 (2002)
Hall, M.: Correlation-based Feature Selection in Machine Learning. PhD thesis, University of Waikato (1999)
Hilario, M., Kalousis, A.: Fusion of meta-knowledge and meta-data for case-based model selection. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 180–191. Springer, Heidelberg (2001)
Hilario, M., Kalousis, A., Nguyen, P., Woznica, A.: A data mining ontology for algorithm selection and meta-mining. In: Workshop on Third-Generation Data Mining: Towards Service-Oriented Knowledge Discovery, SoKD 2009 (2009)
Ho, T.K., Basu, M.: Measures of geometrical complexity in classification problems. In: Data Complexity in Pattern Recognition, ch. 1, pp. 3–23. Springer, Heidelberg (2006)
Hotho, A., Maedche, A., Staab, S., Studer, R.: Seal-II - the soft spot between richly structured and unstructured knowledge. Journal of Universal Computer Science 7(7), 566–590 (2001)
Jankowski, N., Grąbczewski, K.: Building meta-learning algorithms basing on search controlled by machine complexity. In: IEEE World Congress on Computational Intelligence, pp. 3600–3607 (2008)
Kalousis, A.: Algorithm Selection via Meta-Learning. PhD thesis, University of Geneva (2002)
Kalousis, A., Gama, J., Hilario, M.: On data and algorithms: understanding inductive performance. Machine Learning 54, 275–312 (2004)
Kalousis, A., Hilario, M.: Representational issues in meta-learning. In: Proc. of the 20th International Conference on Machine Learning, Washington, DC, Morgan Kaufmann, San Francisco (2003)
Kietz, J.-U., Serban, F., Bernstein, A., Fischer, S.: Data mining workflow templates for intelligent discovery assistance and auto-experimentation. In: Proc. 3rd Workshop on Third-Generation Data Mining: Towards Service-Oriented Knowledge Discovery (SoKD 2010), pp. 1–12 (2010)
Kira, K., Rendell, L.: The feature selection problem: traditional methods and a new algorithm. In: Proc. Nat. Conf. on Artificial Intelligence (AAAI 1992), pp. 129–134 (1992)
Köpf, C., Keller, J.: Meta-analysis: from data characterization for meta-learning to meta-regression. In: PKDD 2000 Workshop on Data Mining, Decision Support, Meta-Learning and ILP (2000)
Leite, R., Brazdil, P.: Predicting a relative performance of classifiers from samples. In: Proc. International Conference on Machine Learning (2005)
Ler, D., Koprinska, I., Chawla, S.: Utilising regression-based landmarkers within a meta-learning framework for algorithm selection. In: Proc. ICML 2005 Workshop on Meta-Learning, pp. 44–51 (2005)
Liu, H., Setiono, R.: A probabilistic approach to feature selection—a filter solution. In: Proc. 13th International Conference on Machine Learning (ICML 1996), Bari, Italy, pp. 319–327 (1996)
Michie, D., Spiegelhalter, D.J., Taylor, C.C. (eds.): Machine learning, neural and statistical classification. Ellis-Horwood (1994)
Mitchell, T.M.: The need for biases in learning generalizations. Technical report, Rutgers University, New Brunswick, NJ (1980)
Morik, K., Scholz, M.: The MiningMart Approach to Knowledge Discovery in Databases. In: Intelligent Technologies for Information Analysis, Springer, Heidelberg (2004)
Panov, P., Dzeroski, S., Soldatova, L.: Ontodm: An ontology of data mining. In: Proceedings of the 2008 IEEE International Conference on Data Mining Workshops, pp. 752–760 (2008)
Pearl, J.: Heuristics: intelligent search strategies for computer problem solving. Addison-Wesley, Reading (1984)
Peng, Y., Flach, P., Brazdil, P., Soares, C.: Decision tree-based data characterization for meta-learning. In: 2nd International Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning (2002)
Peng, Y., Flach, P., Soares, C., Brazdil, P.: Improved dataset characterisation for meta-learning. In: Discovery Science, pp. 141–152 (2002)
Pfahringer, B., Bensusan, H., Giraud-Carrier, C.: Meta-learning by landmarking various learning algorithms. In: Proc. Seventeenth International Conference on Machine Learning, ICML 2000, pp. 743–750. Morgan Kaufmann, San Francisco (2000)
Piatetskey-Shapiro, G.: Data mining and knowledge discovery: The third generation. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1997. LNCS, vol. 1325, Springer, Heidelberg (1997)
Quinlan, J.R.: Improved use of continuous attributes in c4.5. Journal of Artificial Intelligence Research 4, 77–90 (1996)
Rector, A.: Modularisation of domain ontologies implemented in description logics and related formalisms including OWL. In: Proc. International Conference on Knowledge Capture, K-CAP 2003 (2003)
Rendell, L., Seshu, R., Tcheng, D.: Layered concept-learning and dynamically variable bias management. In: Proc. of the 10th International Joint Conference on Artificial Intelligence, pp. 308–314 (1987)
Rice, J.: The algorithm selection problem. Advances in Computing 15, 65–118 (1976)
Schaffer, C.: A conservation law for generalization performance. In: Proc. of the 11th International Conference on Machine Learning, pp. 259–265 (1994)
Sikonja, M.R., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning 53, 23–69 (2003)
Skiena, S.: Implementing discrete mathematics: combinatorics and graph theory with Mathematica. Addison-Wesly Longman Publishing Co., Inc., Boston (1991)
Smith-Miles, K.A.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys 41(1) (2008)
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)
Soares, C., Brazdil, P., Kuba, P.: A meta-learning method to select the kernel width in support vector regression. Machine Learning 54(3), 195–209 (2004)
Souto, M., Prudêncio, R., Soares, R., Araújo, D., Costa, I., Ludermir, T., Schliep, A.: Ranking and selecting clustering algorithms using a meta-learning approach. In: International Joint Conference on Neural Networks (2008)
Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Proc.5th International Conference on Extending Database Technology, pp. 3–17. Springer, Heidelberg (1996)
Suyama, A., Yamaguchi, T.: Specifying and learning inductive learning systems using ontologies. In: Working Notes from the 1998 AAAI Workshop on the Methodology of Applying Machine Learning: Problem Definition, Task Decomposition and Technique Selection (1998)
Todorovski, L., Sžeroski, S.: Experiments in meta-level learning with ILP. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 98–106. Springer, Heidelberg (1999)
Tsymbal, A., Puuronen, S., Terziyan, V.Y.: Arbiter meta-learning with dynamic selection of classifiers and its experimental investigation. In: Advances in Databases and Information Systems, pp. 205–217 (1999)
Utgoff, P.E.: Machine learning of inductive bias. Kluwer Academic Publishers, Dordrecht (1986)
Utgoff, P.E.: Shift of bias for inductive learning. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning. An Artificial Intelligence Approach, ch. 5, vol. 2, pp. 107–148. Morgan Kaufmann, San Francisco (1986)
Vanschoren, J., Soldatova, L.: Exposé: An ontology for data mining experiments. In: International Workshop on Third Generation Data Mining: Towards Service-oriented Knowledge Discovery (SoKD 2010) (September 2010)
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., Soares, C.: Using meta-learning to support data mining. International Journal of Computer Science and Applications 1(1), 31–45 (2004)
Wolpert, D.: The lack of a priori distinctions between learning algorithms. Neural Computation 8(7), 1381–1390 (1996)
Yang, Q., Wu, X.: Ten challenging problems in data mining research. International Journal of Inform 5, 594–604 (2006)
Zaki, M.: Efficiently mining frequent trees in a forest: algorithms and applications. IEEE Transactions on Knowledge and Data Engineering 17 (2005)
Zakova, M., Kremen, P., Zelezny, F., Lavrac, N.: Automating knowledge discovery workflow composition through ontology-based planning. IEEE Transactions on Automation Science and Engineering (2010)
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Hilario, M., Nguyen, P., Do, H., Woznica, A., Kalousis, A. (2011). Ontology-Based Meta-Mining of Knowledge Discovery Workflows. 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_9
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