Meta-learning architectures: Collecting, organizing and exploiting meta-knowledge

Studies in Computational Intelligence

Volumn 358, Issue , 2011, Pages 117-155

  Vanschoren, Joaquin ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 79961093432     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-20980-2_4     Document Type: Article

References (73)

1. Aha, D.: Generalizing from case studies: A case study. In: Proceedings of the Ninth International Conference on Machine Learning, pp. 1-10 (1992)
2. Asuncion, A., Newman, D.: Uci machine learning repository. University of California, School of Information and Computer Science (2007)
3. Bensusan, H., Giraud-Carrier, C: Discovering task neighbourhoods through landmark learning performances. In: Zighed, D.A., Komorowski, J., ytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 325-330. Springer, Heidelberg (2000)
4. 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) (Pubitemid 47290043)
5. 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) (Pubitemid 40495593)
6. 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., Mladenic, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 6-17. Springer, Heidelberg (2007)
7. Brazdil, P., Giraud-Carrier, C, Soares, C, Vilalta, R.: Metalearning: Applications to data mining. Springer, Heidelberg (2009)
8. 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)
9. Chandrasekaran, B., Josephson, J.: What are ontologies, and why do we need them? IEEE Intelligent systems 14(1), 20-26 (1999)
10. 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
11. 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)
12. 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)
13. 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)
14. 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)
15. 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. WC Working Draft (2003), http://www.w.org/TR/2003/WD-owl-ref-20030331
16. 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)
17. Fikes, R., Nilsson, N.: Strips: A new approach to the application of theorem proving to problem solving. Artificial intelligence 2, 189-208 (1971)
18. Foster, I.: Service-oriented science, science 308(5723), 814 (2005)
19. 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)
20. Giraud-Carrier, C: Metalearning-a tutorial. Tutorial at the 2008 International Conference on Machine Learning and Applications, ICMLA 2008 (2008)
21. 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)
22. Grabczewski, 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)
23. 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)
24. 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)
25. Hilario, M., Kalousis, A.: Building algorithm profiles for prior model selection in knowledge discovery systems. Engineering Intelligent Systems 8(2) (2000)
26. 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)
27. Homann, J., Nebel, B.: The ff planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14, 253-302 (2001)
28. Horrocks, I., Patel-Schneider, P., Boley, H.: Swrl: A semantic web rule language combining owl and ruleml. WC Member submission (2004), http://www.w.org/Submissions/SWRL/
29. Kalousis, A.: Algorithm selection via meta-learning. PhD Thesis. University of Geneve (2002)
30. 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)
31. Kalousis, A., Hilario, M.: Model selection via meta-learning: a comparative study. International Journal on Artificial Intelligence Tools 10(4), 525-554 (2001)
32. 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) (Pubitemid 33260725)
33. 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)
34. 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)
35. 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)
36. 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)
37. 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)
38. 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)
39. Levin, L.: Universal sequential search problems. Problemy Peredachi Informat-sii 9(3), 115-116 (1973)
40. Li, M., Vitányi, P.: An introduction to kolmogorov complexity and its applications. In: Text and Monographs in Computer Science. Springer, Heidelberg (1993)
41. Lindner, G., Studer, R.: Ast: Support for algorithm selection with a cbr approach. In: Zytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 418-423. Springer, Heidelberg (1999)
42. 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) (Pubitemid 350149761)
43. 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)
44. 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)
45. Michie, D., Spiegelhalter, D., Taylor, C: Machine learning. In: Neural and Statistical Classification. Ellis Horwood (1994)
46. MLT. Machine learning toolbox. Esprit Framework II Research Project Nr. 2154 (1993)
47. Morik, K., Scholz, M.: The miningmart approach to knowledge discovery in databases. Intelligent Technologies for Information Analysis, pp. 47-65 (2004)
48. 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)
49. Panov, P., Soldatova, L.N., Dzeroski, 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)
50. 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)
51. 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)
52. Rendell, L., Sesł, 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)
53. De Roure, D., Goble, C, Stevens, R.: The design and realisation of the myexperi-ment virtual research environment for social sharing of workflows. Future Generation Computer Systems 25, 561-567 (2009)
54. Rowe, A., Kalaitzopoulos, D., Osmond, M.: The discovery net system for high throughput bioinformatics. Bioinformatics 19, 225-231 (2003)
55. Sacerdoti, E.: Planning in a hierarchy of abstraction spaces. Artificial intelligence 5(2), 115-135 (1974)
56. 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)
57. 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)
58. Soldatova, L., King, R.: An ontology of scientific experiments. Journal of the Royal Society Interface 3(11), 795-803 (2006) (Pubitemid 44901310)
59. 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)
60. 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)
61. Utgoff, P.: Shift of bias for inductive concept learning. In: Machine learning: An artificial intelligence approach, vol. II. Morgan Kaufmann, San Francisco (1986)
62. 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)
63. 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)
64. Vanschoren, J., Blockeel, H.: A community-based platform for machine learning experimentation. In: Buntine, W., Grobelnik, M., Mladenic, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. Lecture Notes in Computer Science (LNAI), vol. 5782, pp. 750-754. Springer, Heidelberg (2009)
65. 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)
66. 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)
67. 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)
68. 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., Zytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 243-253. Springer, Heidelberg (1997) (Pubitemid 127097509)
69. 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)
70. 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)
71. Zhong, N., Liu, C, Ohsuga, S.: Dynamically organizing kdd processes. International Journal of Pattern Recognition and Artificial Intelligence 15(3), 451-473 (2001) (Pubitemid 32609056)
72. 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)
73. Zhong, N., Ohsuga, S.: The gls discovery system: its goal, architecture and current results. In: Ras, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 233-244. Springer, Heidelberg (1994)