Why is rule learning optimistic and how to correct it

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

Volumn 4212 LNAI, Issue , 2006, Pages 330-340

  Možina, Martin ^a   Demšar, Janez ^a   Žabkar, Jure ^a   Bratko, Ivan ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBABILITY;

DATA SETS; EVALUATION FUNCTIONS; PROBABILITY PREDICTIONS;

LEARNING SYSTEMS;

EID: 33750337560     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11871842_33     Document Type: Conference Paper

References (16)

1. Kendall E. Atkinson. An Introduction to Numerical Analysis. John Wiley and Sons, New York, 1989.
2. B. Cestnik. Estimating probabilities: A crucial task in machine learning. In Proceedings of the Ninth European Conference on Artificial Intelligence, pages 147-149, 1990.
3. Peter Clark and Robin Boswell. Rule induction with CN2: Some recent improvements. In Machine Learning - Proceeding of the Fifth Europen Conference (EWSL-91), pages 151-163, Berlin, 1991.
4. Peter Clark and Tim Niblett. The CN2 induction algorithm. Machine Learning Journal, 4(3):261-283, 1989.
5. J. Demšar and B. Zupan. Orange: From experimental machine learning to interactive data mining. White Paper [http://www.ailab.si/orange], Faculty of Computer and Information Science, University of Ljubljana, 2004.
6. Ted E. Dunning. Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1):61-74, 1993.
7. R.A. Fisher and L.H.C. Tippett. Limiting forms of the frequency distribution of the largest and smallest member of a sample. Proc. Camb. Phil. Soc., 24:180-190, 1928.
8. Johannes Fuernkranz and Peter A. Flach. Roc 'n' rule learning - towards a better understanding of covering algorithms. Machine Learning, 58(1):39-77, January 2005.
9. Emil J. Gumbel. Statistical theory of extreme values and some practical applications. National Bureau of Standards Applied Mathematics Series (US Government Printing Office), 33, 1954.
10. Emil J. Gumbel and J. Lieblein. Some applications of extreme-value models. American Statistician, 8(5):14-17, 1954.
11. Shanti S. Gupta. Order statistics from the gamma distribution. Technometrics, 2:243-262, 1960.
12. David D. Jensen and Paul R. Cohen. Multiple comparisons in induction algorithms. Machine Learning, 38(3):309-338, March 2000.
13. Nada Lavrač, Peter Flach, and Blaž Zupan. Rule evaluation measures: A unifying view. In Saša Džeroski and Peter Flach, editors, Proceedings of the 9th International Workshop on Inductive Logic Programming (ILP-99), pages 174-185, Bled, Slovenia, 1999.
14. Wentian Li, Fengzhu Sun, and Ivo Grosse. Extreme value distribution based gene selection criteria for discriminant microarray data analysis using logistic regression. Journal of Computational Biology, 11(2/3):215-226, 2004.
15. P. M. Murphy and D. W. Aha. UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/mlrepository.html]. Irvine, CA: University of California, Department of Information and Computer Science, 1994.
16. Ljupčo Todorovski, Peter Flach, and Nada Lavrač. Predictive performance of weighted relative accuracy. In D. Zighed, J. Komorowski, and J. Zytkow, editors, Proceedings of the 4th European Conference of Principles of Data Mining and Knowledge Discovery (PKDD-00), pages 255-264, Lyon, France, 2000.