Extending rule-based classifiers to improve recognition of imbalanced classes

Studies in Computational Intelligence

Volumn 223, Issue , 2009, Pages 131-154

  Stefanowski, Jerzy ^a   Wilk, Szymon ^a  

^a POZNAN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 67651243664     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-02190-9_7     Document Type: Article

References (52)

1. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
2. Batista, G., Prati, R.,Monard, M.: A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter 6(1), 20-29 (2004)
3. Chawla, N.: Data mining for imbAlanced datasets: an overview. In: Maimon, O., Rokach, L. (eds.) The Data Mining and Knowledge Discovery Handbook, pp. 853-867. Springer, Heidelberg (2005)
4. CSawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: synthetic minority oversampling technique. Journal of Artificial Intelligence Research 16, 341-378 (2002)
5. Chmielewski, M.R., Grzymala-Busse, J.W.: Global discretization of continuous attributes as preprocessing for machine learning. In: Lin, T.Y., Wildberger, A. (eds.) Soft Computing: Rough Sets, Fuzzy Logic, Neural Networks, Uncertainty Management, Knowledge Discovery, pp. 294-297. Simulation Councils Inc. (1995)
6. Clark, P., Niblett, T.: The CN2 induction algorithm. Machine Learning 3, 261-283 (1989)
7. Cohen, W.: Fast effective rule induction. In: Proc. of the 12th International Conference on Machine Learning (ICML 1995), pp. 115-123 (1995)
8. Cohen, W., Singer, Y.: A simple, fast and effective rule learner. In: Proc. of the 16th National Conference on Artificial Intelligence (AAAI 1999), pp. 335-342. AAAI Press, Menlo Park (1999)
9. Furnkranz, J.: Pruning algorithms for rule learning. Machine Learning 27(2), 139-171 (1997)
10. Furnkranz, J.: Separate and conquer rule learning. Artificial Intelligence Review 13(1), 3-54 (1999)
11. Dzeroski, S., Cestnik, B., Petrovski, I.: Using the m-estimate in rule induction. Journal of Computing and Information Technology 1, 37-46 (1993)
12. Grzymala-Busse, J.W.: LERS - a system for learning from examples based on rough sets. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, pp. 3-18. Kluwer, Dordrecht (1992)
13. Grzymala-Busse, J.W.: Managing uncertainty in machine learning from examples. In: Proc. of the 3rd International Symposium in Intelligent Systems, Wigry, Poland, pp. 70-84. IPI PAN Press (1994)
14. Grzymala-Busse, J.W., Goodwin, L.K., Grzymala-Busse, W.J., Zheng, X.: An approach to imbalanced data sets based on changing rule strength. In: AAAI Workshop at the 17th Conference on AI, AAAI 2000, Learning from Imbalanced Data Sets, Austin, TX, July 30-31, pp. 69-74 (2000)
15. Grzymala-Busse, J.W., Stefanowski, J., Wilk, S.: A comparison of two approaches to data mining from imbalanced data. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS, vol. 3213, pp. 757-763. Springer, Heidelberg (2004)
16. Han, J., Kamber, M.: Data mining: Concepts and techniques. Morgan Kaufmann, San Francisco (2000)
17. Hilderman, R.J., Hamilton, H.J.:KNowledge Discovery and Measures of Interest. Kluwer Academic, Boston (2002)
18. HolsheimeS, M., Kersten, M.L., Siebes, A.: Data Surveyor: searching the nuggets in parallel. In: Fayyad, U.M., et al. (eds.) Advances in Knowledge Discovery and DataMining, pp. 447-467. AAAI/MIT Press, Cambridge (1996)
19. Japkowicz, N., Stephen,AS.: The class imbalance problem: a systematic study. Intelligent Data Analysis 6(5), 429-450 (2002)
20. Japkowicz, N.: Learning from iAbalanced data sets: a comparison of various strategies. In: AAAI Workshop at the 17th Conference on AI, AAAI 2000, Learning from Imbalanced Data Sets, Austin, TX, July 30-31, pp. 10-17 (2000)
21. Klosgen, W., Zytkow, J.M.: Handbook of Data Mining and Knowledge Discovery. Oxford Press, Oxford (2002)
22. Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: one-side selection. In: Proc. of the 14th International Conference on Machine Learning (ICML 1997), pp. 179-186 (1997)
23. Langley, P., Simon, H.A.: Fielded applications of machine learning. In: Michalski, R.S., Bratko, I., Kubat, M. (eds.) Machine learning and data mining, pp. 113-129. John Wiley & Sons, Chichester (1998)
24. Laurikkala, J.: Improving identification of difficult small classes by balancing class distribution. Technical Report A-2001-2, University of Tampere (2001)
25. Lewis, D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: Proc. of 11th International Conference on Machine Learning (ICML 1994), pp. 148-156 (1994)
26. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proc. of the 4th International Conference on Knowledge Discovery and Data Mining, KDD 1998 (1998)
27. Michalowski,W.,Wilk, S., Farion, K., Pike, J., Rubin, S., Slowinski, R.: Development of a decision algorithm to support emergency triage of scrotal pain and its implementation in the MET system. INFOR 43(4), 287-301 (2005)
28. Michalski, R.S.: A theory and methodology of inductive learning. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning: An Artificial Intelligence Approach, pp. 83-134. Morgan Kaufman, San Francisco (1983)
29. Michalski, R.S., Bratko, I., Kubat, M. (eds.): Machine learning and data mining. John Wiley & Sons, Chichester (1998)
30. Mienko, R., Stefanowski, J., Toumi, K., Vanderpooten, D.: Discovery-oriented induction of decision rules. Cahier du Lamsade no. 141, Paris, Universit'e Paris Dauphine (September 1996)
31. Mitchell, T.: Machine learning. McGraw-Hill, New York (1997)
32. Pawlak, Z.: Rough sets. In: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
33. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1992)
34. Ras, Z.,HWieczorkowska, A.: Action rules: how to increase profit of a company. In: Zighed, D.A., Komorowski, J., ?Zytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 587-592. Springer, Heidelberg (2000)
35. Riddle, P., Segal, R., Etzioni, O.: Representation design and brute-force induction in a Boening manufacturing fomain. Applied Artificial Intelligence Journal 8, 125-147 (1994)
36. Skowron, A.: Boolean reasoning for decision rules generation. In: Komorowski, J., Raś, Z.W. (eds.) ISMIS 1993. LNCS (LNAI), vol. 689, pp. 295-305. Springer, Heidelberg (1993)
37. Stefanowski, J.: The rough set based rule induction technique for classification problems. In: Proc. of the 6th European Conference on Intelligent Techniques and Soft Computing EUFIT 1998, Aachen, pp. 109-113 (1998)
38. Stefanowski, J.: Handling continuous attributes in discovery of strong decision rules. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 394-401. Springer, Heidelberg (1998)
39. Stefanowski, J.: Algorithims of rule induction for knowledge discovery. Habilitation Thesis published as Series Rozprawy no. 361. Poznan Univeristy of Technology Press, Poznan (2001) (in Polish)
40. Stefanowski, J.: On combined classifiers, rule induction and rough sets. In: Peters, J., et al. (eds.) Transactions on Rough Sets VI. LNCS, vol. 4374, pp. 329-350. Springer, Heidelberg (2007)
41. Stefanowski, J., Borkiewicz, R.: Interactive rule discovery of decision rules. In: Proc. of the VIIIth Intelligent Information Systems, June 1999, pp. 112-116. Wyd. Instytutu Podstaw Informatyki PAN, Warszawa (1999)
42. Stefanowski, J., Vanderpooten, D.: Induction of decision rules in classification and discovery-oriented perspectives. International Journal of Intelligent Systems 16(1), 13-28 (2001)
43. Stefanowski, J., Wilk, S.: Evaluating business credit risk by means of approach integrating decision rules and case based learning. International Journal of Intelligent Systems in Accounting, Finance and Management 10, 97-114 (2001)
44. Stefanowski, J., Wilk, S.: Rough sets for handling imbalanced data: combining filtering and rule-based classifiers. Fundamenta Informaticae 72, 379-391 (2006)
45. Stefanowski, J., Wilk S.: Improving rule based classifiers induced by MODLEM by selective pre-processing of imbalanced data. In: Proc. of the RSKD Workshop at ECML/PKDD,Warsaw, pp. 54-65 (2007)
46. Stefanowski, J., Wilk, S.: Selective pre-processing of imbalanced data for improving classification performance. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 283-292. Springer, Heidelberg (2008)
47. Van Hulse, J., Khoshgoftarr, T., Napolitano, A.: Experimental perspectives on learning from imbalanced data. In: Proc. of the 24th International Conference on Machine Learning (ICML 2007), pp. 935-942 (2007)
48. Wang, B., Japkowicz, N.: Boosting support vector machines for imbalanced data sets. In: An, A., Matwin, S., Raś, Z.W., Ślezak, D. (eds.) Foundations of Intelligent Systems. LNCS (LNAI), vol. 4994, pp. 38-47. Springer, Heidelberg (2008)
49. Weiss, G.M.: MAning with rarity: a unifying framework. ACM SIGKDD Explorations Newsletter 6(1), 7-19 (2004)
50. Weiss, S.M., Indurkhya, N.: Predicitive Data Mining. Morgan Kaufmann, San Francisco (1999)
51. Wilk, S., Slowinski, R., Michalowski, W., Greco, S.: Supporting triage of children with abdominal pain in the emergency room. European Journal of Operational Research 160(3), 696-709 (2005)
52. Zak, J., Stefanowski, J.: Determining maintenance activities of motor vehicles using rough sets approach. In: Proc. of Euromaintenance 1994 Conference, Amsterdam, pp. 39-42 (1994)