Abstract
While a valid intellectual challenge in its own right, meta-learning finds its real raison d’être in the practical support it offers Data Mining practitioners [20]. Indeed, the whole point of understanding how to learn in any given situation is to go out in the real world and learn as much as possible, from any source of data we encounter! However, almost any type of raw data will initially be very hard to learn from, and about 80% of the effort in discovering useful patterns lies in the clever preprocessing of data [47]. Thus, for machine learning to become a tool we can instantly apply in any given situation, or at least to get proper guidance when applying it, we need to build extended meta-learning systems that encompass the entire knowledge discovery process, from raw data to finished models, and that keep learning, keep accumulating meta-knowledge, every time they are presented with new problems.
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References
Aha, D.: Generalizing from case studies: A case study. In: Proceedings of the Ninth International Conference on Machine Learning, pp. 1–10 (1992)
Asuncion, A., Newman, D.: Uci machine learning repository. University of California, School of Information and Computer Science (2007)
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)
Bernstein, A., Dänzer, M.: The nExT system: Towards true dynamic adaptations of semantic web service compositions. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 739–748. Springer, Heidelberg (2007)
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)
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., Giraud-Carrier, C., Soares, C., Vilalta, R.: Metalearning: Applications to data mining. Springer, Heidelberg (2009)
Brazdil, P., Soares, C., Costa, J.P.D.: Ranking learning algorithms: Using ibl and meta-learning on accuracy and time results. Machine Learning 50, 251–277 (2003)
Chandrasekaran, B., Josephson, J.: What are ontologies, and why do we need them? IEEE Intelligent systems 14(1), 20–26 (1999)
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: Crisp-dm 1.0. a step-by-step data mining guide (1999), http://www.crisp-dm.org
Charest, M., Delisle, S.: Ontology-guided intelligent data mining assistance: Combining declarative and …. In: Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Soft Computing, pp. 9–14 (2006)
Charest, M., Delisle, S., Cervantes, O., Shen, Y.: Intelligent data mining assistance via cbr and ontologies. In: Proceedings of the 17th International Conference on Database and Expert Systems Applications (DEXA 2006) (2006)
Charest, M., Delisle, S., Cervantes, O., Shen, Y.: Bridging the gap between data mining and decision support: A case-based reasoning and …. Intelligent Data Analysis 12, 1–26 (2008)
Craw, S., Sleeman, D., Graner, N., Rissakis, M.: Consultant: Providing advice for the machine learning toolbox. In: Research and Development in Expert Systems IX: Proceedings of Expert Systems 1992, pp. 5–23 (1992)
Dean, M., Connolly, D., van Harmelen, F., Hendler, J., Horrocks, I., Orah, L., McGuinness, D., Patel-Schneider, P.F., Stein, L.A.: Web ontology language (owl) reference version 1.0. W3C Working Draft (2003), http://www.w3.org/TR/2003/WD-owl-ref-20030331
Engels, R.: Planning tasks for knowledge discovery in databases; performing task-oriented user-guidance. In: Proceedings of the 2nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 170–175 (1996)
Fikes, R., Nilsson, N.: Strips: A new approach to the application of theorem proving to problem solving. Artificial intelligence 2, 189–208 (1971)
Foster, I.: Service-oriented science. science 308(5723), 814 (2005)
Giraud-Carrier, C.: The data mining advisor: meta-learning at the service of practitioners. In: Proceedings of the 4th International Conference on Machine Learning and Applications, pp. 113–119 (2005)
Giraud-Carrier, C.: Metalearning-a tutorial. Tutorial at the 2008 International Conference on Machine Learning and Applications, ICMLA 2008 (2008)
Grabczewski, K., Jankowski, N.: Versatile and efficient meta-learning architecture: Knowledge representation and …. In: IEEE Symposium on Computational Intelligence and Data Mining, pp. 51–58 (2007)
Grąbczewski, K., Jankowski, N.: Meta-learning with machine generators and complexity controlled exploration. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 545–555. Springer, Heidelberg (2008)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The weka data mining software: An update. SIGKDD Explorations 11(1), 10–18 (2009)
Hidalgo, M., Menasalvas, E., Eibe, S.: Definition of a metadata schema for describing data preparation tasks. In: Proceedings of the ECML/PKDD 2009 Workshop on 3rd generation Data Mining (SoKD 2009), pp. 64–75 (2009)
Hilario, M., Kalousis, A.: Building algorithm profiles for prior model selection in knowledge discovery systems. Engineering Intelligent Systems 8(2) (2000)
Hilario, M., Kalousis, A., Nguyen, P., Woznica, A.: A data mining ontology for algorithm selection and meta-mining. In: Proceedings of the ECML/PKDD 2009 Workshop on 3rd generation Data Mining (SoKD 2009), pp. 76–87 (2009)
Homann, J., Nebel, B.: The ff planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14, 253–302 (2001)
Horrocks, I., Patel-Schneider, P., Boley, H.: Swrl: A semantic web rule language combining owl and ruleml. W3C Member submission (2004), http://www.w3.org/Submissions/SWRL/
Kalousis, A.: Algorithm selection via meta-learning. PhD Thesis. University of Geneve (2002)
Kalousis, A., Bernstein, A., Hilario, M.: Meta-learning with kernels and similarity functions for planning of data mining workflows. In: ICML/COLT/UAI 2008 Planning to Learn Workshop (PlanLearn), pp. 23–28 (2008)
Kalousis, A., Hilario, M.: Model selection via meta-learning: a comparative study. International Journal on Artificial Intelligence Tools 10(4), 525–554 (2001)
Kalousis, A., Theoharis, T.: Noemon: Design, implementation and performance results of an intelligent assistant for classifier selection. Intelligent Data Analysis 3(4), 319–337 (1999)
Kaufman, K.: Inlen: a methodology and integrated system for knowledge discovery in databases. PhD Thesis, School of Information Technology and Engineering, George Mason University (1997)
Kaufman, K., Michalski, R.: Discovery planning: Multistrategy learning in data mining. In: Proceedings of the Fourth International Workshop on Multistrategy Learning, pp. 14–20 (1998)
Kietz, J., Serban, F., Bernstein, A., Fischer, S.: Towards cooperative planning of data mining workflows. In: Proceedings of the Third Generation Data Mining Workshop at the 2009 European Conference on Machine Learning (ECML 2009), pp. 1–12 (2009)
Klusch, M., Gerber, A., Schmidt, M.: Semantic web service composition planning with owls-xplan. In: Proceedings of the First International AAAI Fall Symposium on Agents and the Semantic Web (2005)
Kodratoff, Y., Sleeman, D., Uszynski, M., Causse, K., Craw, S.: Building a machine learning toolbox. In: Enhancing the Knowledge Engineering Process: Contributions from ESPRIT, pp. 81–108 (1992)
Le-Khac, N., Kechadi, M., Carthy, J.: Admire framework: Distributed data mining on data grid platforms. In: Proceedings of the 1st International Conference on Software and Data Technologies, vol. 2, pp. 67–72 (2006)
Levin, L.: Universal sequential search problems. Problemy Peredachi Informatsii 9(3), 115–116 (1973)
Li, M., Vitányi, P.: An introduction to kolmogorov complexity and its applications. In: Text and Monographs in Computer Science. Springer, Heidelberg (1993)
Lindner, G., Studer, R.: Ast: Support for algorithm selection with a cbr approach. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 418–423. Springer, Heidelberg (1999)
Liu, Z., Ranganathan, A., Riabov, A.: A planning approach for message-oriented semantic web service composition. In: Proceedings of the National Conference on AI, vol. 5(2), pp. 1389–1394 (2007)
METAL. Metal: A meta-learning assistant for providing user support in machine learning and data mining. ESPRIT Framework IV LRT Reactive Project Nr. 26.357 (2001)
Michalski, R., Kerschberg, L., Kaufman, K.: Mining for knowledge in databases: The inlen architecture, initial implementation and first results. Journal of Intelligent Information Systems 1(1), 85–113 (1992)
Michie, D., Spiegelhalter, D., Taylor, C.: Machine learning. In: Neural and Statistical Classification. Ellis Horwood (1994)
MLT. Machine learning toolbox. Esprit Framework II Research Project Nr. 2154 (1993)
Morik, K., Scholz, M.: The miningmart approach to knowledge discovery in databases. Intelligent Technologies for Information Analysis, pp. 47–65 (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)
Panov, P., Soldatova, L.N., Džeroski, S.: Towards an ontology of data mining investigations. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS, vol. 5808, pp. 257–271. Springer, Heidelberg (2009)
Pfahringer, B., Bensusan, H., Giraud-Carrier, C.: Meta-learning by landmarking various learning algorithms. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 743–750 (2000)
Podpecan, V., Jursic, M., Zakova, M., Lavrac, N.: Towards a service-oriented knowledge discovery platform. In: Proceedings of the SoKD 2009 International Workshop on Third Generation Data Mining at ECML PKDD 2009, pp. 25–38 (2009)
Rendell, L., Seshu, R., Tcheng, D.: Layered concept learning and dynamically-variable bias management. In: Proceedings of the 10th International Joint Conference on Artificial Intelligence, pp. 308–314 (1987)
De Roure, D., Goble, C., Stevens, R.: The design and realisation of the myexperiment virtual research environment for social sharing of workflows. Future Generation Computer Systems 25, 561–567 (2009)
Rowe, A., Kalaitzopoulos, D., Osmond, M.: The discovery net system for high throughput bioinformatics. Bioinformatics 19, 225–231 (2003)
Sacerdoti, E.: Planning in a hierarchy of abstraction spaces. Artificial intelligence 5(2), 115–135 (1974)
Sirin, E., Parsia, B., Wu, D., Hendler, J., Nau, D.: Htn planning for web service composition using shop2. Journal of Web Semantics 1(4), 377–396 (2004)
Sleeman, D., Rissakis, M., Craw, S., Graner, N., Sharma, S.: Consultant-2: Pre-and post-processing of machine learning applications. International Journal of Human-Computer Studies 43(1), 43–63 (1995)
Soldatova, L., King, R.: An ontology of scientific experiments. Journal of the Royal Society Interface 3(11), 795–803 (2006)
Talia, D., Trunfio, P., Verta, O.: Weka4ws: a wsrf-enabled weka toolkit for distributed data mining on grids. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 309–320. Springer, Heidelberg (2005)
Taylor, I., Shields, M., Wang, I., Harrison, A.: The triana workflow environment: Architecture and applications. In: Workflows for e-Science, pp. 320–339. Springer, Heidelberg (2007)
Utgoff, P.: Shift of bias for inductive concept learning. In: Machine learning: An artificial intelligence approach, vol. II. Morgan Kaufmann, San Francisco (1986)
Van Someren, M.: Towards automating goal-driven learning. In: Proceedings of the Planning to Learn Workshop at the 18th European Conference of Machine Learning (ECML 2007), pp. 42–52 (2007)
Vanschoren, J., Assche, A.V., Vens, C., Blockeel, H.: Meta-learning from experiment databases: An illustration. In: Proceedings of the 16th Annual Machine Learning Conference of Belgium and The Netherlands (Benelearn 2007), pp. 120–127 (2007)
Vanschoren, J., Blockeel, H.: A community-based platform for machine learning experimentation. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. Lecture Notes in Computer Science (LNAI), vol. 5782, pp. 750–754. Springer, Heidelberg (2009)
Vanschoren, J., Blockeel, H., Pfahringer, B.: Experiment databases: Creating a new platform for meta-learning research. In: Proceedings of the ICML/UAI/COLT Joint Planning to Learn Workshop (PlanLearn 2008), pp. 10–15 (2008)
Vanschoren, J., Blockeel, H., Pfahringer, B., Holmes, G.: Organizing the world’s machine learning information. Communications in Computer and Information Science 17, 693–708 (2008)
Vanschoren, J., Pfahringer, B., Holmes, G.: Learning from the past with experiment databases. In: Ho, T.-B., Zhou, Z.-H. (eds.) PRICAI 2008. LNCS (LNAI), vol. 5351, pp. 485–496. Springer, Heidelberg (2008)
Wirth, R., Shearer, C., Grimmer, U., Reinartz, T., Schlosser, J., Breitner, C., Engels, R., Lindner, G.: Towards process-oriented tool support for knowledge discovery in databases. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 243–253. Springer, Heidelberg (1997)
Zakova, M., Kremen, P., Zelezny, F., Lavrac, N.: Planning to learn with a knowledge discovery ontology. In: Second Planning to Learn Workshop at the Joint ICML/COLT/UAI Conference, pp. 29–34 (2008)
Záková, M., Podpecan, V., Zelezný, F., Lavrac, N.: Advancing data mining workflow construction: A framework and cases using the orange toolkit. In: Proceedings of the SoKD-2009 International Workshop on Third Generation Data Mining at ECML PKDD 2009, pp. 39–51 (2009)
Zhong, N., Liu, C., Ohsuga, S.: Dynamically organizing kdd processes. International Journal of Pattern Recognition and Artificial Intelligence 15(3), 451–473 (2001)
Zhong, N., Matsui, Y., Okuno, T., Liu, C.: Framework of a multi-agent kdd system. In: Yin, H., Allinson, N.M., Freeman, R., Keane, J.A., Hubbard, S. (eds.) IDEAL 2002. LNCS, vol. 2412, pp. 337–346. Springer, Heidelberg (2002)
Zhong, N., Ohsuga, S.: The gls discovery system: its goal, architecture and current results. In: Raś, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 233–244. Springer, Heidelberg (1994)
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Vanschoren, J. (2011). Meta-Learning Architectures: Collecting, Organizing and Exploiting Meta-Knowledge. 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_4
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