On the quest for optimal rule learning heuristics

Machine Learning

Volumn 78, Issue 3, 2010, Pages 343-379

  Janssen, Frederik ^a   Fürnkranz, Johannes ^a  

^a DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Heuristics; Inductive rule learning; Metalearning

Indexed keywords

ECONOMIC AND SOCIAL EFFECTS; HEURISTIC ALGORITHMS;

COMPARATIVE EVALUATIONS; DEFAULT VALUES; HEURISTIC RULES; HEURISTICS; INDUCTIVE RULE LEARNING; METALEARNING; OPTIMAL PARAMETER SETTINGS; SEARCH HEURISTICS;

OPTIMIZATION;

EID: 78650715148     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-009-5162-2     Document Type: Article

References (46)

1. Akaike, H. (1974). A new look at the statistical model selection. IEEE Transactions on Automatic Control, 19(6), 716-723.
2. Asuncion, A., & Newman, D. (2007). UCI machine learning repository. http://archive.ics.uci.edu/ml/.
3. Bayardo, R. Jr., & Agrawal, R. (1999). Mining the most interesting rules. In Proceedings of the 5th ACM SIGKDD international conference on knowledge discovery and data mining (KDD-97) (pp. 145-154).
4. Brin, S., Motwani, R., & Silverstein, C. (1997). Beyond market baskets: generalizing association rules to correlations. In Proceedings of the ACM SIGMOD international conference on management of data (pp. 265-276). (Pubitemid 127456791)
5. Buntine,W., & Niblett, T. (1992). A further comparison of splitting rules for decision-tree induction. Machine Learning, 8, 75-85.
6. Burges, S. (2006). Meta-Lernen einer Evaluierungs-Funktion für einen Regel-Lerner. Master?s thesis, TU Darmstadt, December 2006 (in German) (English title: Meta-learning of an evaluation function for a rule learner).
7. Cestnik, B. (1990). Estimating probabilities: a crucial task in machine learning. In L. Aiello (Ed.), Proceedings of the 9th European conference on artificial intelligence (pp. 147-150). ECAI-90, Stockholm, Sweden, 1990. London: Pitman.
8. Clark, P., & Boswell, R. (1991). Rule induction with CN2: Some recent improvements. In Proceedings of the 5th European working session on learning (pp. 151-163). EWSL-91, Porto, Portugal, 1991. Berlin: Springer.
9. Clark, P., & Niblett, T. (1989). The CN2 induction algorithm. Machine Learning, 3(4), 261-283.
10. Cohen, W. W. (1995). Fast effective rule induction. In A. Prieditis & S. Russell (Eds.), Proceedings of the 12th international conference on machine learning (pp. 115-123). Tahoe City, CA, July 9-12, 1995. San Mateo: Morgan Kaufmann.
11. Demsar, J. (2006). Statistical comparisons of classifiers over multiple datasets. Journal of Machine Learning Research, 7, 1-30. (Pubitemid 43022939)
12. Fan, R.-E., Chen, P.-H., Lin, C.-J., & Joachims, T. (2005). Working set selection using the second order information for training SVM. Journal of Machine Learning Research, 6, 1889-1918. (Pubitemid 41798130)
13. Frank, E., & Witten, I. H. (1998). Generating accurate rule sets without global optimization. In J. Shavlik (Ed.), Proceedings of the 15th international conference on machine learning (pp. 144-151). ICML-98, Madison, WI, 1998. San Mateo: Morgan Kaufmann.
14. Fürnkranz, J. (1999). Separate-and-conquer rule learning. Artificial Intelligence Review, 13(1), 3-54.
15. Fürnkranz, J. (2004). Modeling rule precision. In J. Fürnkranz (Ed.), Proceedings of the ECML/PKDD-04 workshop on advances in inductive rule learning (pp. 30-45). Pisa, Italy, 2004.
16. Fürnkranz, J. (2004). FOSSIL: A robust relational learner. In F. Bergadano & L. De Raedt (Eds.), Lecture notes in artificial intelligence: Vol. 784. Proceedings of the 7th European conference on machine learning (pp. 122-137). ECML-94, Catania, Italy, 1994. Berlin: Springer.
17. Fürnkranz, J. (1997). Pruning algorithms for rule learning. Machine Learning, 27(2), 139-171.
18. Fürnkranz, J., & Flach, P. (2004). An analysis of stopping and filtering criteria for rule learning. In J.-F. Boulicaut, F. Esposito, F. Giannotti, & D. Pedreschi (Eds.), Lecture notes in artificial intelligence: Vol. 3201. Proceedings of the 15th European conference on machine learning (pp. 123-133). ECML-04, Pisa, Italy, 2004. Berlin: Springer. (Pubitemid 41050086)
19. Fürnkranz, J., & Flach, P. A. (2005). ROC ?n? rule learning-towards a better understanding of covering algorithms. Machine Learning, 58(1), 39-77. (Pubitemid 40356739)
20. Fürnkranz, J., & Widmer, G. (1994). Incremental reduced error pruning. In W. Cohen & H. Hirsh (Eds.), Proceedings of the 11th international conference on machine learning (pp. 70-77). ML-94, New Brunswick, NJ, 1994. San Mateo: Morgan Kaufmann.
21. Holte, R., Acker, L., & Porter, B. (1989). Concept learning and the problem of small disjuncts. In Proceedings of the 11th international joint conference on artificial intelligence (pp. 813-818). IJCAI-89, Detroit, MI, 1989. San Mateo: Morgan Kaufmann.
22. Janssen, F., & Fürnkranz, J. (2007). On meta-learning rule learning heuristics. In Proceedings of the 7th IEEE conference on data mining (pp. 529-534). ICDM-07, Omaha, NE, 2007.
23. Janssen, F., & Fürnkranz, J. (2008). An empirical investigation of the trade-off between consistency and coverage in rule learning heuristics. In T. Horvath, J.-F. Boulicaut, & M. Berthold (Eds.), Proceedings of the 11th international conference on discovery science (pp. 40-51). DS-08, Budapest, Hungary, 2008. Berlin: Springer.
24. Janssen, F., & Fürnkranz, J. (2009). A re-evaluation of the over-searching phenomenon in inductive rule learning. In Proceedings of the SIAM international conference on data mining (pp. 329-340). SDM-09, Sparks, NV, 2009.
25. Klösgen, W. (1992). Problems for knowledge discovery in databases and their treatment in the statistics interpreter explora. International Journal of Intelligent Systems, 7, 649-673.
26. Lavrač, N., Flach, P., & Zupan, B. (1999). Rule evaluation measures: a unifying view. In S. Džeroski & P. Flach (Eds.), Proceedings of the 9th international workshop on inductive logic programming (ILP-99) (pp. 174-185). Berlin: Springer.
27. Lavrač, N., Kavšek, B., Flach, P., & Todorovski, L. (2004). Subgroup discovery with CN2-SD. Journal of Machine Learning Research, 5, 153-188.
28. Lavrač, N., Cestnik, B., & Džeroski, S. (1992a). Search heuristics in empirical inductive logic programming. In Logical approaches to machine learning, workshop notes of the 10th European conference on AI, Vienna, Austria, 1992.
29. Lavrač, N., Cestnik, B., & Džeroski, S. (1992b). Use of heuristics in empirical inductive logic programming. In S. H. Muggleton & K. Furukawa (Eds.), Proceedings of the 2nd international workshop on inductive logic programming (ILP-92), Number TM-1182 in ICOT Technical Memorandum, Tokyo, Japan, 1992. Institute for New Generation Computer Technology.
30. Michalski, R. S. (1969). On the quasi-minimal solution of the covering problem. In Proceedings of the 5th international symposium on information processing (pp. 125-128). Switching Circuits, Vol. A3, FCIP-69, Bled, Yugoslavia, 1969.
31. Mingers, J. (1989). An empirical comparison of selection measures for decision-tree induction. Machine Learning, 3, 319-342.
32. Mozina, M., Demšar, J., Zabkar, J., & Bratko, I. (2006). Why is rule learning optimistic and how to correct it. In Machine learning: ECML 2006, 17th European conference on machine learning (pp. 330-340). (Pubitemid 44618843)
33. Muggleton, S. H. (1995). Inverse entailment and Progol. New Generation Computing, 13(3, 4), 245-286. Special issue on inductive logic programming.
34. Quinlan, J. (1996). Learning first-order definitions of functions. Journal of Artificial Intelligence Research, 5, 139-161. (Pubitemid 126646167)
35. Quinlan, J. R. (1990). Learning logical definitions from relations. Machine Learning, 5, 239-266.
36. Quinlan, J. R. (1983). Learning efficient classification procedures and their application to chess end games. In R. S. Michalski, J. G. Carbonell, & T.M.Mitchell (Eds.), Machine learning. An artificial intelligence approach (pp. 463-482). Palo Alto: Tioga.
37. Salton, G., & McGill, M. J. (1986). Introduction to modern information retrieval. New York: McGraw-Hill.
38. Scheffer, T. (2005). Finding association rules that trade support optimally against confidence. Intelligent Data Analysis, 9(3), 381-395.
39. Tan, P.-N., Kumar, V., & Srivastava, J. (2002). Selecting the right interestingness measure for association patterns. In Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 32-41). KDD-02, Edmonton, Alberta, 2002.
40. Thiel, M. (2005). Separate and Conquer Framework und disjunktive Regeln. Master?s thesis, TU Darmstadt, 2005. In German (English title: Separate and conquer framework and disjunctive rules).
41. Todorovski, L., Flach, P., & Lavrac, N. (2000). Predictive performance of weighted relative accuracy. In D. A. Zighed, J. Komorowski, & J. Zytkow (Eds.), 4th European conference on principles of data mining and knowledge discovery (PKDD2000) (pp. 255-264). Berlin: Springer.
42. Vapnik, V., Levin, E., & Cun, Y. L. (1994). Measuring the VC-dimension of a learning machine. Neural Computation, 6(5), 851-876.
43. Witten, I. H., & Frank, E. (2005). Data mining-practical machine learning tools and techniques with java implementations (2nd edn.). San Mateo: Morgan Kaufmann. http://www.cs.waikato.ac.nz/∼ml/weka/.
44. Wrobel, S. (1997). An algorithm for multi-relational discovery of subgroups. In J. Komorowski & J. Zytkow (Eds.), Proc. first European symposium on principles of data mining and knowledge discovery (pp. 78-87). PKDD-97, Berlin, 1997. Berlin: Springer. (Pubitemid 127097494)
45. Wu, T., Chen, Y., & Han, J. (2007). Association mining in large databases: a re-examination of its measures. In Proceedings of the 11th European symposium on principles of data mining and knowledge discovery (pp. 621-628). PKDD-07, Warsaw, Poland, 2007. Berlin: Springer.
46. Xiong, H., Shekhar, S., Tan, P.-N., & Kumar, V. (2004). Exploiting a support-based upper bound of Pearson?s correlation coefficient for efficiently identifying strongly correlated pairs. In Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 334-343). KDD-04, Seattle, USA, 2004. (Pubitemid 40114943)