Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection

Expert Systems with Applications

Volumn 35, Issue 4, 2008, Pages 1949-1965

  Cano, José Ramón ^a   Herrera, Francisco ^b   Lozano, Manuel ^b   García, Salvador ^b  

^a UNIVERSITY OF JAÉN   (Spain)

^b UNIVERSITY OF GRANADA   (Spain)

Author keywords

Instance selection; Scaling down; Subgroup discovery

Indexed keywords

DIFFUSERS (OPTICAL); POPULATION STATISTICS;

DATA SETS; INSTANCE SELECTION; LARGE DATA; LARGE SIZES; SCALING DOWN; SCALING PROBLEMS; STATISTICAL CHARACTERISTICS; SUBGROUP DISCOVERY;

SET THEORY;

EID: 48749106976     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.08.083     Document Type: Article

References (57)

1. Anderson T.W. An introduction to multivariate statistical analysis (1984), John Wiley and Sons
2. Atzmueller, M., & Puppe, F. (2006). SD-Map a fast algorithm for exhaustive subgroup discovery. In Proceedings of the 10th European conference on principles and practice of knowledge discovery in databases (pp. 6-17).
3. Back T., Fogel D., and Michalewicz Z. Handbook of evolutionary computation (1997), Oxford University Press
4. Berlanga F., Del Jesus M.J., Gonzalez P., Herrera F., and Mesonero M. Multiobjective evolutionary induction of subgroup discovery fuzzy rules: A case study in marketing. Lecture Notes in Computer Science 4065 (2006) 337-349
5. Brightom H., and Mellish C. Advances in instance selection for instance based learning algorithms. Data Mining and Knowledge Discovery 6 (2002) 153-172
6. Cano J.-R., Herrera F., and Lozano M. Using evolutionary computation as instance selection for data reduction in KDD: An experimental study. IEEE Transactions on Evolutionary Computation 7 6 (2003) 561-575
7. Cano J.-R., Herrera F., and Lozano M. Stratification for scaling up evolutionary prototype selection. Pattern Recognition Letters 26 (2005) 953-963
8. Cano J.-R., Herrera F., and Lozano M. Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability. Data and Knowledge Engineering 60 1 (2007) 90-108
9. Chang Y.-C., Lai P.-C., and Lee M.-T. An integrated approach for operational knowledge acquisition of refuse incinerators. Expert Systems with Applications 33 2 (2007) 413-419
10. Clark P., and Boswell R. The CN2 induction algorithm. Machine Learning 3 4 (1989) 261-283
11. Clark, P., & Niblett, T. (1991). Rule induction with CN2: Some recent improvements. In Proceedings of the fifth European working session on learning (pp. 151-163).
12. Demsar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006) 1-30
13. Domingo C., Gavaldá R., and Watanabe O. Adaptive sampling methods for scaling up knowledge discovery algorithms. Data Mining and Knowledge Discovery 6 2 (2002) 131-152
14. Eshelman L.J. The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. Foundation of Genetic Algorithms 1 (1991) 265-283
15. Friedman M. A comparison of alternative tests of significance for the problem of m rankings. Annals of Mathematical Statistics 11 (1940) 86-92
16. Gamberger, D., & Lavrač, N. (2002). Generating actionable knowledge by expert-guided subgroup discovery. In Proceedings of principles of data mining knowledge discovery (pp. 163-174).
17. Gamberger, D., & Lavrač, N. (2002). Descriptive induction through subgroup discovery: A case study in a medical domain. In Proceedings of the international conference on machine learning (pp. 163-170).
18. Gamberger D., and Lavrač N. Expert-guided subgroup discovery: Methodology and application. Journal of Artificial Intelligence Research 17 (2002) 501-527
19. Gamberger, D., Lavrač, N., & Wettschereck, D. (2002). Subgroup visualization: A method and application in population screening. In Proceedings of the intelligent data analysis in medicine and pharmacology (pp. 31-35).
20. Grochowski, M., & Jankowski, N. (2004). Comparison of instance selection algorithms II. Results and comments. In Proceedings of the 7th international conference on artificial intelligence and soft computing (pp. 580-585).
21. Han J., and Kamber M. Data mining: Concepts and techniques (2000), Morgan Kaufman
22. Hart P.E. The condesed nearest neighbour rule. IEEE Transactions on Information Theory 18 3 (1968) 431-433
23. Holm S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6 (1979) 65-70
24. Huang C.-L., and Wang C.-J. A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Application 31 2 (2006) 231-240
25. Iman R.L., and Davenport J.M. Approximations of the critical region of the Friedman statistic. Communications in Statistics (1980) 571-595
26. Janssens D., Brijs T., Vanhoof K., and Wets G. Evaluating the perfomance of cost-based discretization versus entropy- and error-based discretization. Computers and Operations Research 33 (2006) 3107-3123
27. Kavšek, B., Lavrač, N., & Bullas, J. C. (2002). Rule induction for subgroup discovery: A case study in mining UK traffic accident data. In Proceedings of the international multi-conference on information society (pp. 127-130).
28. Kavšek B., and Lavrač N. APRIORI-SD: Adapting association rule learning to subgroup discovery. Applied Artificial Intelligence 20 7 (2006) 543-583
29. Kibbler, D., & Aha, D. W. (1987). Learning representative exemplars of concepts: An initial case of study. In Proceedings of the 4th international workshop on machine learning (pp. 24-30).
30. Kim K.-J. Articial neural networks with evolutionary instance selection for financial forecasting. Expert Systems with Applications 30 3 (2006) 519-526
31. Klöesgen W. Explora: A multipattern and multistrategy discovery assistant. Advances in knowledge discovery and data mining (1996), MIT Press pp. 249-271
32. Klöesgen, W., & Michael, M. (2002). Census data mining - An application. In Proceedings of the 6th European conference on principles of data mining, knowledge discovery (pp. 65-79).
33. Kuncheva L. Editing for the k-nearest neighbors rule by a genetic algorithm. Pattern Recognition Letters 16 (1995) 809-814
34. Lavrač, N., Flach, P., Kavšek, B., & Todorovski, L. (2002). Adapting classification rule induction to subgroup discovery. In Proceedings of the IEEE international conference on data mining (pp. 266-273).
35. Lavrač N., Cestnik B., Gamberger D., and Flach P. Decision support through subgroup discovery: Three case studies and the lessons learned. Machine Learning 57 (2004) 115-143
36. Lavrač N., Kavšek B., Flach P., and Todorovski L. Subgroup discovery with CN2-SD. Journal of Machine Learning Research 5 (2004) 153-188
37. Lavrač, N. (2005). Subgroup discovery techniques and applications. In Proceedings of the 19th Pacific-Asia conference advances in knowledge discovery (pp. 2-14).
38. In: Liu H., and Motoda H. (Eds). Instance selection and construction for data mining (2001), Kluwer Academic Publishers
39. Liu H., Hussain F., Tan C.L., and Dash M. Discretization: An enabling technique. Data Mining and Knowledge Discovery 6 4 (2002) 393-423
40. Liu H., and Motoda H. On issues of instance selection. Data Mining and Knowledge Discovery 6 (2002) 114-130
41. Nakada, T., & Kunifuji, S. (2003). Subgroup discovery among personal homepages. In Proceedings of the discovery science (pp. 385-392).
42. Newman, D. J., Hettich, S., Blake, C. L. & Merz, C. J. (1998). UCI Repository of machine learning databases, http://www.ics.uci.edu/~mlearn/MLRepository.html, University of California, Irvine, Department of Information and Computer Sciences.
43. Polat K., and Gunes S. Medical decision support system based on artificial immune recognition immune system (AIRS), fuzzy weighted pre-processing and feature selection. Expert Systems with Applications 33 2 (2007) 484-490
44. Provost F., and Kolluri V. A survey of methods for scaling up inductive algorithms. Data Mining and Knowledge Discovery 3 2 (1999) 131-169
45. Riquelme J.C., Aguilar J.S., and Toro M. Finding representative patterns with ordered projections. Pattern Recognition 36 (2003) 1009-1018
46. Sánchez J.S. High training set size reduction by space partitioning and prototype abstraction. Pattern Recognition 37 (2004) 1561-1564
47. Sebban M., Nock R., Chauchat J.H., and Rakotomalala R. Impact of learning set quality and size on decision tree perfomances. International Journal of Computers, Systems and Signals 1 1 (2000) 85-105
48. Shang W., Huang H., Zhu H., Lin Y., Qu Y., and Wang Z. A novel feature selection algorithm for text categorization. Expert Systems with Applications 33 1 (2006) 1-5
49. Sheskin D.J. Handbook of parametric and nonparametric statistical procedures (2000), CRC Press
50. Wilcoxon F. Individual comparisons by ranking methods. Biometrics 1 (1945) 80-83
51. Wilson D.L. Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man and Cybernetics 2 3 (1972) 408-421
52. Wilson, D. R., & Martinez, T. R. (1997). Instance pruning techniques. In Proceedings of the 14th international conference (pp. 403-411).
53. Wilson D.R., and Martinez T.R. Reduction techniques for instance-based learning algorithms. Machine Learning 38 (2000) 257-268
54. Wrobel, S. (1997). An algorithm for multi-relational discovery of subgroups. In Proceedings of the 1st European conference on principles of data mining and knowledge discovery (pp. 78-87).
55. Yen S.-J., and Lee Y.-S. An efficient data mining approach for discovering interesting knowledge from customer transactions. Expert Systems with Applications 30 4 (2006) 650-657
56. Zar J.H. Biostatistical analysis (1999), Prentice Hall
57. Železný F., and Lavrač N. Propositionalization-based relational subgroup discovery with RSD. Machine Learning 62 (2006) 33-63