Is the UCI repository useful for data mining?

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2902, Issue , 2003, Pages 209-223

  Soares, Carlos ^a  

^a UNIVERSITY OF PORTO   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

BENCH-MARK PROBLEMS; OVERFITTING; PRACTICAL USE; REAL-WORLD; RELATIVE PERFORMANCE; UCI REPOSITORY;

DATA MINING;

EID: 0346325856     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24580-3_28     Document Type: Article

References (31)

1. S. Bay, D. Kibler, M. Pazzani, and P. Smyth. The UCI KDD archive of large data sets for data mining research and experimentation. Information Processing Society of Japan Magazine, 42(5), 2001.
2. C. Blake and C. Merz. Repository of machine learning databases, 1998. http:/www.ics.uci.edu/∼mlearn/MLRepository.html.
3. R. Brachman, T. Khabaza, W. Kloesgen, G. Piatetsky-Shapiro, and E. Simoudis. Mining business databases. Communications of the ACM, 39(11):42-48, 1996.
4. P. Brazdil, J. Gama, and B. Henery. Characterizing the applicability of classification algorithms using meta-level learning. In F. Bergadano and L. de Raedt, editors, Proceedings of the European Conference on Machine Learning (ECML-94), pages 83-102. Springer-Verlag, 1994.
5. P. Brazdil, C. Soares, and J. Costa. Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results. Machine Learning, 50(3):251-277, 2003.
6. C. Brodley. Addressing the selective superiority problem: Automatic Algorithm/Model class selection. In P. Utgoff, editor, Proceedings of the Tenth International Conference on Machine Learning, pages 17-24. Morgan Kaufmann, 1993.
7. P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth. CRISP-DM 1.0: Step-by-Step Data Mining Guide. SPSS, 2000.
8. W. Cohen. Fast effective rule induction. In A. Prieditis and S. Russell, editors, Proceedings of the 11th International Conference on Machine Learning, pages 115-123. Morgan Kaufmann, 1995.
9. T. Dietterich. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7):1895-1924, 1998. ftp://ftp.cs.orst.edu/pub/tgd/papers/nc-stats.ps.gz.
10. J. Gama. Probabilistic linear tree. In D. Fisher, editor, Proceedings of the 14th International Machine Learning Conference (ICML97), pages 134-142. Morgan Kaufmann, 1997.
11. J. Gehrke. Report on the SIGKDD 2001 conference panel "new research directions on KDD". SIGKDD Explorations, 3(2), 2002.
12. M. Hilario and A. Kalousis. Building algorithm profiles for prior model selection in knowledge discovery systems. In Proceedings of the IEEE SMC'99 International Conference on Systems, Man and Cybernetics. IEEE Press, 1999.
13. R. Ihaka and R. Gentleman. R: A language for data analysis and graphics. Journal of Computational and Graphics and Statistics, 5(3):299-314, 1996.
14. J. Keller, I. Holzer, and S. Silvery. Using data envelopment analysis and case-based reasoning techniques for knowledge-based engine intake port design. In Proceedings of the International Conference on Engineering Design (ICED99), 1999.
15. E. Keogh and S. Kasetty. On the need for time series data mining benchmarks: A survey and empirical demonstration. In Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 102-111. ACM, 2002.
16. R. Kohavi, D. Sommerfield, and J. Dougherty. Data mining using MLC++: A machine learning library in c++. International Journal on Artificial Intelligence Tools, 6(4):537-566, 1997. Available at http://robotics.stanford.edu/users/ronnyk/mlcj.ps.gz.
17. P. Langley. Crafting papers on machine learning. In P. Langley, editor, Proceedings of the Seventeenth International Conference on Machine Learning, pages 1207-1212. Morgan Kaufmann, 2000.
18. T.-S. Lim, W.-Y. Loh, and Y.-S. Shih. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40:203-229, 2000.
19. G. Lindner and R. Studer. Forecastig the fault rate behavior for cars. In Proceedings of the "From Machine Learning to Data Mining and Knowledge Discovery" ICML 99 Workshop, 1999.
20. METAL Consortium. Esprit project METAL (#26.357). 2002.
21. D. Michie, D. Spiegelhalter, and C. Taylor. Machine Learning, Neural and Statistical Classification. Ellis Horwood, 1994.
22. H. Neave and P. Worthington. Distribution-Free Tests. Routledge, 1992.
23. M. Oldemiro and L. Torgo. Predicting daily returns for the ibm stock. In L. Torgo, editor, Proceedings of the Workshop on Artificial Intelligence Techniques for Financial Time Series Analysis. Available from http://www.liacc.up.pt/∼ltorgo/AIFTSA, 2001.
24. J. Petrak. MLEE: Machine Learning Experimentation Environment. OFAI, 2002. http://www.ai.univie.ac.at/∼johann/mlee.html.
25. F. Provost and R. Kohavi. On applied research in machine learning. Machine Learning, 30(2/3):127-132, 1998.
26. R. Quinlan. C5.0: An Informal Tutorial. RuleQuest, 1998. http://www.rulequest.com/see5-unix.html.
27. L. Saitta and F. Neri. Learning in the "real world". Machine Learning, 30(2/3):133-163, 1998.
28. S. Salzberg. On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery, 1:317-327, 1997. http://www.cs.jhu.edu/∼salzberg/critique.ps.
29. M. Staudt, J.-U. Kietz, and U. Reimer. A data mining support environment and its application on insurance data. In D. Heckerman, H. Mannila, D. Pregibon, and R. Uthurusamy, editors, Proceedings of the Fourth International Conference on Knowledge Discovery in Databases & Data Mining, pages 105-111. AAAI Press, 1998.
30. D. Wolpert. The lack of a priori distinctions between learning algorithms. Neural Computation, 8:1341-1390, 1996.
31. M. Zaki. Editorial: Online, interactive and anytime data mining. SIGKDD Explorations, 3(2), 2002.