A comparative study on rough set based class imbalance learning

Knowledge-Based Systems

Volumn 21, Issue 8, 2008, Pages 753-763

  Liu, Jinfu ^a   Hu, Qinghua ^a   Yu, Daren ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Class imbalance learning; Rough sets; Sample weighting

Indexed keywords

DECISION MAKING; DECISION THEORY; DECISION TREES; EDUCATION; FUZZY SETS; LEARNING SYSTEMS; MATHEMATICAL MODELS; SAMPLING; SET THEORY; SUPPORT VECTOR MACHINES;

CLASS IMBALANCE LEARNING; CLASS IMBALANCES; COMPARATIVE STUDIES; EXHAUSTIVE SET OF RULES; FILTERING; GREAT-ER; MAJORITY VOTING; OPTIMAL CONFIGURATIONS; RE-SAMPLING; ROUGH SETS; RULE EXTRACTIONS; SAMPLE WEIGHTING; VARIOUS CONFIGURATIONS; WEIGHTED ATTRIBUTES; WEIGHTED DEGREES; WEIGHTED RULES;

ROUGH SET THEORY;

EID: 54949132937     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2008.03.031     Document Type: Article

References (60)

1. Batista G., Prati R.C., and Monard M.C. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6 1 (2004) 20-29
2. Bazan J. A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables. In: Polkowski L., and Skowron A. (Eds). Rough Sets in Knowledge Discovery (1998), Physica-Verlag, Heidelberg 321-365
3. Beynon M. Reducts within the variable precision rough sets model: a further investigation. European Journal of Operational Research 134 (2001) 592-605
4. C. Blake, E. Keogh, C.J. Merz, UCI Repository of Machine Learning Databases, Department of Information and Computer Science, University of California, Irvine. Available from: , 1998.
5. U. Brefeld, P. Geibel, F. Wysotzki, Support vector machines with sample dependent costs, Proceedings of 14th European Conference on Machine Learning, 2003, pp. 23-34.
6. N. Chawla, N. Japkowicz, A. Kolcz (Eds.), ICML'2003 Workshop on Learning from Imbalanced Data Sets (II), Proceedings available at: , 2003.
7. Cortes C., and Vapnik V. Support-vector network. Machine Learning 20 (1995) 273-297
8. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (2000), Cambridge University Press, Cambridge
9. C. Drummond, R.C. Holte, C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling, Working Notes of the ICML'03 Workshop Learning from Imbalanced Data Sets, 2003.
10. Duntsch I., and Gediga G. Uncertainty measures of rough set prediction. Artificial Intelligence 106 1 (1998) 109-137
11. Fawcett R.E., and Provost F. Adaptive fraud detection. Data Mining and Knowledge Discovery 3 1 (1997) 291-316
12. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters 27 (2006) 861-874
13. Fayyad U., and Irani K. Multi-interval discretization of continuous-valued attributes for classification learning. Proceeding of the 13th International Joint Conference on Artificial Intelligence (1993), Morgan Kaufman pp. 1022-1027
14. Greco S., Matarazzo B., and Slowinski R. Rough set theory for multicriteria decision analysis. European Journal of Operational Research 129 (2001) 1-47
15. Guiasu S. Information Theory with Applications (1977), McGraw-Hill, International Book Company, New York
16. Grzymala-Busse D.M., and Grzymala-Busse J.W. The usefulness of machine learning approach to knowledge acquisition. Computational Intelligence 11 (1995) 268-279
17. Grzymala-Busse J.W. LERS- a system for learning from samples based on rough sets. In: Slowinski R. (Ed). Intelligent Decision Support (1992), Kluwer Academic Publishers 3-18
18. Hand D.J. Construction and Assessment of Classification Rules (1997), John Wiley and Sons
19. Hand D.J., and Till R.J. A simple generalization of the area under the ROC curve to multiple class classification problems. Machine Learning 45 2 (2001) 171-186
20. Hu X.-H., and Cercone N. Data mining via discretization, generalization and rough set feature selection. Knowledge and Information Systems 1 1 (1999) 33-60
21. N. Japkowicz, Learning from imbalanced data sets: a comparison of various strategies, Working Notes of the AAAI'00 Workshop Learning from Imbalanced Data Sets, 2000, pp. 10-15.
22. N. Japkowicz (Eds.), AAAI Workshop on Learning from Imbalanced Data Sets, Technical Report WS-00-05, AAAI Press, Menlo Park, CA, 2003.
23. Japkowicz N., and Stephen S. The class imbalance problem: a systematic study. Intelligent Data Analysis 6 5 (2002) 429-450
24. Kryszkiewicz M. Rules in incomplete information systems. Information Sciences 113 3-4 (1999) 271-292
25. Kryszkiewicz M. Comparative study of alternative type of knowledge reduction in inconsistent systems. International Journal of Intelligent Systems 16 (2001) 105-120
26. Liang J.-Y., and Xu Z.-B. The algorithm on knowledge reduction in incomplete information systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10 (2002) 95-103
27. Liu H., Hussain F., Tan C.L., and Dash M. Discretization: an enabling technique. Data Mining and Knowledge Discovery 6 (2002) 393-423
28. Liu J.F., Hu Q.H., and Yu D.R. A weighted rough set based method developed for class imbalance learning. Information Sciences 178 4 (2008) 1235-1256
29. M.A. Maloof, Learning when data sets are imbalanced and when costs are unequal and unknown, in: Proceedings of Working Notes ICML'03 Workshop Learning from Imbalanced Data Sets, 2003.
30. Mi J.-S., Wu W.-Z., and Zhang W.-X. Approaches to knowledge reduction based on variable precision rough set model. Information Sciences 159 (2004) 255-272
31. Michalski R.S. A theory and methodology of inductive learning. In: Michalski R.S., Carbonell J.G., and Mitchell T.M. (Eds). Machine Learning: An Artificial Intelligence Approach (1983), Morgan Kaufman, San Mateo, CA 83-134
32. Osuna E., Freund R., and Girosi F. Support vector machines: training and applications. AI Memo 1602 (1997), Massachusetts Institute of Technology
33. Pawlak Z. Rough sets. International Journal of Computer and Information Sciences 11 (1982) 341-356
34. Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning about Data (1991), Kluwer Academic Publishers, Dordrecht
35. Pawlak Z. Rough sets and intelligent data analysis. Information Sciences 147 (2002) 1-12
36. Pawlak Z., Grzymala-Busse J.W., Slowinski R., and Ziarko W. Rough sets. Communications of the ACM 38 11 (1995) 89-95
37. Pawlak Z., and Skowron A. Rough sets: some extensions. Information Sciences 177 1 (2007) 28-40
38. Pawlak Z., and Skowron A. Rudiments of rough sets. Information Sciences 177 1 (2007) 3-27
39. Pawlak Z., and Skowron A. Rough sets and boolean reasoning. Information Sciences 177 1 (2007) 41-73
40. R.C. Prati, G.E.A.P.A. Batista, M.C. Monard, Class imbalances versus class overlapping: an analysis of a learning system behavior, MICAI, 2004, pp. 312-321.
41. F.J. Provost, T. Fawcett, Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions, KDD, 1997, pp. 43-48.
42. Quinlan J.R. C4.5 Programs for Machine Learning (1988), Morgan Kaufman, CA
43. G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C. Spyropoulos, P. atamatopoulos, Stacking classifiers for anti-spam filtering of E-mail, in: L. Lee, D. Harman (Eds.), Proceedings of the 6th Conference on Empirical Methods in Natural Language Processing (EMNLP 2001), Carnegie Mellon University, 2001, pp. 44-50.
44. Shen Q., and Chouchoulas A. A rough-fuzzy approach for generating classification rules. Pattern Recognition 35 11 (2002) 2425-2438
45. Slezak D. Approximate reducts in decision tables. Proceedings of Sixth Internation Conference on Information Management of Uncertainty in Knowledge-based System (1996), Granada
46. Slezak D. Approximate entropy reducts. Fundamenta Informaticae 53 3-4 (2002) 365-390
47. Stefanowski J. On rough set based approaches to induction of decision rules. In: Polkowski L., and Skowron A. (Eds). Rough Sets in Knowledge Discovery (1998), Physica-Verlag, Heidelberg, Germany 501-529
48. Stefanowski J., and Vanderpooten D. A general two stage approach to rule induction from examples. In: Ziarko W. (Ed). Rough Sets, Fuzzy Sets and Knowledge Discovery (1994), Springer-Verlag, London, UK 317-325
49. Stefanowski J., and Wilk S. Rough sets for handling imbalanced data: combining filtering and rule-based classifiers. Fundamenta Informaticae 72 1 (2006) 379-391
50. Tao Q., Wu G.-W., Wang F.-Y., and Wang J. Posterior probability support vector machines for unbalanced data. IEEE Transactions on Neural Networks 16 6 (2005) 1561-1573
51. Ting K.M. An instance-weighting method to induce cost-sensitive trees. IEEE Transactions on Knowledge and Data Engineering 14 3 (2002) 659-665
52. Tsumoto S. Automated extraction of medical expert system rules from clinical databases based on rough set theory. Information Sciences 112 1-4 (1998) 67-84
53. Tsumoto S. Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model. Information Sciences 162 2 (2004) 65-80
54. Vapnik V. Statistical Learning Theory (1998), Wiley, New York, NY
55. Wang G.Y., Zhao J., An J.J., et al. A comparative study of algebra viewpoint and information viewpoint in attribute reduction. Fundamenta Informaticae 68 3 (2005) 289-301
56. Weiss G.M. Mining with rarity - problems and solutions: a unifying framework. SIGKDD Explorations 6 1 (2004) 7-19
57. G.M. Weiss, F. Provost, The effect of class distribution on classifier learning: an empirical study, Technical Report ML-TR-44, Rutgers University, Department of Computer Science, 2001.
58. B. Zadrozny, C. Elkan, Learning and making decisions when costs and probabilities are both unknown, Technical Report CS2001-0664, UCSD, 2001.
59. Zhou Z.-H., and Liu X.-Y. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering 18 1 (2006) 63-77
60. Ziarko W. Variable precision rough set model. Journal of Computer and System Sciences 46 (1993) 39-59