Feature selection with positive region constraint for test-cost-sensitive data

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

Volumn 8449 LNCS, Issue , 2014, Pages 23-33

  Liu, Jiabin ^a,b   Min, Fan ^b   Zhao, Hong ^b   Zhu, William ^b  

^a Department of Computer Science Sichuan University for Nationalities ^*  (China)

^b MINNAN NORMAL UNIVERSITY   (China)

Author keywords

Cost sensitive learning; Feature selection; Positive region; Test cost

Indexed keywords

ARTIFICIAL INTELLIGENCE; BUDGET CONTROL; FEATURE EXTRACTION; HEURISTIC ALGORITHMS; PARETO PRINCIPLE; ROUGH SET THEORY; TESTING; DATA MINING; LEARNING SYSTEMS;

ATTRIBUTE REDUCTION; COST-SENSITIVE LEARNING; INFORMATION GAIN; MACHINE LEARNING APPLICATIONS; PARETO DISTRIBUTIONS; POSITIVE REGION; TEST COST; UNIVERSITY OF CALIFORNIA;

COSTS; CONSTRAINT SATISFACTION PROBLEMS;

EID: 84907363809     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-662-44680-5_2     Document Type: Conference Paper

References (46)

1. Chen, X.: An improved branch and bound algorithm for feature selection. Pattern Recogn. Lett. 24(12), 1925-1933 (2003)
2. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1, 131-156 (1997)
3. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17, 37-54 (1996)
4. Greco, S., Matarazzo, B., Slowinski, R., Stefanowski, J.: Variable consistency model of dominance-based rough sets approach. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 170-181. Springer, Heidelberg (2001)
5. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998). http://www.ics.uci.edu/~mlearn/mlrepository.html
6. He, H.P., Min, F.: Accumulated cost based test-cost-sensitive attribute reduction. In: Kuznetsov, S.O., Ślȩzak, D., Hepting, D.H., Mirkin, B.G. (eds.) RSFDGrC 2011. LNCS, vol. 6743, pp. 244-247. Springer, Heidelberg (2011)
7. He, H.P., Min, F., Zhu, W.: Attribute reduction in test-cost-sensitive decision systems with common-test-costs. In: Proceedings of the 3rd International Conference on Machine Learning and Computing, vol. 1, pp. 432-436 (2011)
8. Hu, Q.H., Yu, D.R., Liu, J.F., Wu, C.: Neighborhood rough set based heterogeneous feature subset selection. Inf. Sci. 178(18), 3577-3594 (2008)
9. Hunt, E.B., Marin, J., Stone, P.J. (eds.): Experiments in Induction. Academic Press, New York (1966)
10. Lanzi, P.: Fast feature selection with genetic algorithms: a filter approach. In: IEEE International Conference on Evolutionary Computation 1997. IEEE (1997)
11. Lin, T.Y.: Granular computing on binary relations - analysis of conflict and Chinese wall security policy. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 296-299. Springer, Heidelberg (2002)
12. Lin, T.Y.: Granular computing - structures, representations, and applications. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 16-24. Springer, Heidelberg (2003)
13. Liu, Q.H., Li, F., Min, F., Ye, M., Yang, W.G.: An efficient reduction algorithm based on new conditional information entropy. Control Decis. (in Chinese) 20(8), 878-882 (2005)
14. Liu, J.B., Min, F., Liao, S.J., Zhu, W.: A genetic algorithm to attribute reduction with test cost constraint. In: Proceedings of 6th International Conference on Computer Sciences and Convergence Information Technology, pp. 751-754 (2011)
15. Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. The Springer International Series in Engineering and Computer Science, vol. 454. Kluwer Academic Publishers, Boston (1998)
16. Ma, L.W.: On some types of neighborhood-related covering rough sets. Int. J. Approx. Reason. 53(6), 901-911 (2012)
17. Min, F., He, H.P., Qian, Y.H., Zhu, W.: Test-cost-sensitive attribute reduction. Inf. Sci. 181, 4928-4942 (2011)
18. Min, F., Hu, Q.H., Zhu, W.: Feature selection with test cost constraint. Int. J. Approximate Reasoning (2013, to appear). doi:10.1016/j.ijar.2013.04.003
19. Min, F., Liu, Q.H.: A hierarchical model for test-cost-sensitive decision systems. Inf. Sci. 179, 2442-2452 (2009)
20. Min, F., Zhu, W.: Attribute reduction with test cost constraint. J. Electr. Sci. Technol. China 9(2), 97-102 (2011)
21. Min, F., Zhu, W.: Minimal cost attribute reduction through backtracking. In: Kim, T., et al. (eds.) DTA/BSBT 2011. CCIS, vol. 258, pp. 100-107. Springer, Heidelberg (2011)
22. Min, F., Zhu, W.: Optimal sub-reducts in the dynamic environment. In: Proceedings of IEEE International Conference on Granular Computing, pp. 457-462 (2011)
23. Min, F., Zhu, W.: Optimal sub-reducts with test cost constraint. In: Yao, J.T., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 57-62. Springer, Heidelberg (2011)
24. Min, F., Zhu, W., Zhao, H., Pan, G.Y., Liu, J.B., Xu, Z.L.: Coser: cost-sensitive rough sets (2012). http://grc.fjzs.edu.cn/~fmin/coser/
25. Pan, G.Y., Min, F., Zhu, W.: A genetic algorithm to the minimal test cost reduct problem. In: Proceedings of IEEE International Conference on Granular Computing. pp. 539-544 (2011)
26. Pawlak, Z.: Rough set approach to knowledge-based decision support. Eur. J. Oper. Res. 99, 48-57 (1997)
27. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341-356 (1982)
28. Pawlak, Z.: Rough sets and intelligent data analysis. Inf. Sci. 147(12), 1-12 (2002)
29. Qian, Y.H., Liang, J.Y., Pedrycz, W., Dang, C.Y.: Positive approximation: an accelerator for attribute reduction in rough set theory. Artif. Intell. 174(9-10), 597-618 (2010)
30. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Intelligent Decision Support (1992)
31. Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recogn. Lett. 24(6), 833-849 (2003)
32. Tseng, T.L.B., Huang, C.-C.: Rough set-based approach to feature selection in customer relationship management. Omega 35(4), 365-383 (2007)
33. Wang, G.Y.: Attribute core of decision table. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 213-217. Springer, Heidelberg (2002)
34. Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature selection based on rough sets and particle swarm optimization. Pattern Recogn. Lett. 28(4), 459-471 (2007)
35. Xu, Z.L., Min, F., Liu, J.B., Zhu, W.: Ant colony optimization to minimal test cost reduction. In: Proceedings of the 2011 IEEE International Conference on Granular Computing. pp. 688-693 (2012)
36. Yao, J.T., Zhang, M.: Feature selection with adjustable criteria. In: Ślȩzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 204-213. Springer, Heidelberg (2005)
37. Yao, Y.Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Inf. Sci. 178(17), 3356-3373 (2008)
38. Zhang, W.X., Mi, J., Wu, W.: Knowledge reductions in inconsistent information systems. Chin. J. Comput. 26(1), 12-18 (2003)
39. Zhao, H., Min, F., Zhu, W.: A backtracking approach to minimal cost feature selection of numerical data. J. Inf. Comput. Sci. 10(13), 4105-4115 (2013)
40. Zhao, H., Min, F., Zhu, W.: Test-cost-sensitive attribute reduction based on neighborhood rough set. In: Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 802-806 (2011)
41. Zhao, H., Min, F., Zhu, W.: Test-cost-sensitive attribute reduction of data with normal distribution measurement errors. Math. Prob. Eng. 2013, 12 pp (2013)
42. Zhong, N., Dong, Z.J., Ohsuga, S.: Using rough sets with heuristics to feature selection. J. Intell. Inf. Syst. 16(3), 199-214 (2001)
43. Zhu, W.: Generalized rough sets based on relations. Inf. Sci. 177(22), 4997-5011 (2007)
44. Zhu, W.: Topological approaches to covering rough sets. Inf. Sci. 177(6), 1499-1508 (2007)
45. Zhu, W.: Relationship between generalized rough sets based on binary relation and covering. Inf. Sci. 179(3), 210-225 (2009)
46. Zhu, W., Wang, F.: Reduction and axiomization of covering generalized rough sets. Inf. Sci. 152(1), 217-230 (2003)