Fuzzy-rough feature selection accelerator

Fuzzy Sets and Systems

Volumn 258, Issue , 2015, Pages 61-78

  Qian, Yuhua ^a,b   Wang, Qi ^a   Cheng, Honghong ^a   Liang, Jiye ^a   Dang, Chuangyin ^b  

^a SHANXI UNIVERSITY   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Accelerator; Feature selection; Forward approximation; Fuzzy rough sets; Granular computing; Rough sets

Indexed keywords

ACCELERATION; ALGORITHMS; APPROXIMATION ALGORITHMS; DATA MINING; GRANULAR COMPUTING; HEURISTIC ALGORITHMS; HEURISTIC METHODS; NUMERICAL METHODS; PARTICLE ACCELERATORS; ROUGH SET THEORY; SET THEORY;

DATA MINING AND KNOWLEDGE DISCOVERY; DIMENSIONALITY REDUCTION; EFFECTIVE APPROACHES; FEATURE SELECTION ALGORITHM; FORWARD APPROXIMATION; FUZZY-ROUGH SETS; HIGH DIMENSIONALITY; MODIFIED ALGORITHMS;

FEATURE EXTRACTION;

EID: 84911386411     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2014.04.029     Document Type: Article

References (48)

1. T. Beaubouef, F.E. Perty, G. Arora, Information-theoretic measures of uncertainty for rough sets and rough relational databases, Inf. Sci. 109 (1998) 185-195.
2. R.B. Bhatt, M. Gopal, On the compact computational domain of fuzzy-rough sets, Pattern Recognit. Lett. 26 (2005) 1632-1640.
3. O. Boehm, D.R. Hardoon, L.M. Manevitz, Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms, Int. J. Mach. Learn. Cybern. 2(3) (2011) 125-134.
4. D.G. Chen, L. Zhang, S.Y. Zhao, Q.H. Hu, P.F. Zhu, A novel algorithm for finding reducts with fuzzy rough sets, IEEE Trans. Fuzzy Syst. 20(2) (2012) 385-389.
5. D. Dübois, H. Prade, Rough fuzzy sets and fuzzy rough sets, Int. J. Gen. Syst. 17 (1990) 191-209.
6. I. Düntsch, G. Gediga, Uncertainty measures of rough set prediction, Artif. Intell. 106 (1998) 109-137.
7. G. Gediga, I. Düntsch, Rough approximation quality revisited, Artif. Intell. 132 (2001) 219-234.
8. J.W. Guan, D.A. Bell, Rough computational methods for information systems, Artif. Intell. 105 (1998) 77-103.
9. X.H. Hu, N. Cercone, Learning in relational databases: a rough set approach, Int. J. Comput. Intell. 11(2) (1995) 323-338.
10. Q. Hu, W. Pedrycz, D. Yu, J. Lang, Selecting discrete and continuous features based on neighborhood decisionerror minimization, IEEE Trans. Syst. Man Cybern., Part B, Cybern. 40(1) (2010) 137-150.
11. Q. Hu, Z.X. Xie, D.R. Yu, Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation, Pattern Recognit. 40 (2007) 3509-3521.
12. Q. Hu, D. Yu, W. Pedrycz, D. Chen, Kernelized fuzzy rough sets and their applications, IEEE Trans. Knowl. Data Eng. 23(11) (2011) 1649-1667.
13. Q. Hu, D.R. Yu, Z.X. Xie, Information-preserving hybrid data reduction based on fuzzy-rough techniques, Pattern Recognit. Lett. 27(5) (2006) 414-423.
14. Q. Hu, L. Zhang, S. An, D. Zhang, D. Yu, On robust fuzzy rough set models, IEEE Trans. Fuzzy Syst. 20(4) (2012) 636-651.
15. R. Jensen, Q. Shen, Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches, IEEE Trans. Knowl. Data Eng. 16(12) (2004) 1457-1471.
16. R. Jensen, Q. Shen, Fuzzy-rough sets assisted attribute reduction, IEEE Trans. Fuzzy Syst. 15(1) (2007) 73-89.
17. R. Jensen, Q. Shen, New approaches to fuzzy-rough feature selection, IEEE Trans. Fuzzy Syst. 17(4) (2009) 824-838.
18. J.Y. Liang, C.Y. Dang, K.S. Chin, C.M. Yam Richard, A new method for measuring uncertainty and fuzziness in rough set theory, Int. J. Gen. Syst. 31(4) (2002) 331-342.
19. J.Y. Liang, Y.H. Qian, Information granules and entropy theory in information systems, Sci. China, Ser. F 51(10) (2008) 1427-1444.
20. X.D. Liu, W. Pedrycz, T.Y. Chai, M.L. Song, The development of fuzzy rough sets with the use of structures and algebras of axiomatic fuzzy sets, IEEE Trans. Knowl. Data Eng. 21(3) (2009) 443-462.
21. S. Mitra, H. Banka, W. Pedrycz, Rough-fuzzy collaborative clustering, IEEE Trans. Syst. Man Cybern., Part B, Cybern. 36(4) (2006) 795-805.
22. Z. Pawlak, Rough sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publisher, London, 1991.
23. Z. Pawlak, J.W. Grzymala-Busse, R. Slowiski, W. Ziako, Rough sets, Commun. ACM 38(11) (1995) 89-95.
24. W. Pedrycz, A. Bargiela, Granular clustering: a granular signature of data, IEEE Trans. Syst. Man Cybern., Part B, Cybern. 32(2) (2002) 212-224.
25. W. Pedrycz, Z.A. Sosnowski, Designing decision trees with the use of fuzzy granulation, IEEE Trans. Syst. Man Cybern., Part A, Syst. Hum. 3(2) (2000) 151-159.
26. Y.H. Qian, J.Y. Liang, C.Y. Dang, Incomplete multigranulation rough set, IEEE Trans. Syst. Man Cybern., Part A, Syst. Hum. 40(2) (2010) 420-431.
27. Y.H. Qian, J.Y. Liang, W. Pedrycz, C.Y. Dang, Positive approximation: an accelerator for attribute reduction in rough set theory, Artif. Intell. 174 (2010) 597-618.
28. Y.H. Qian, J.Y. Liang, W. Pedrycz, C.Y. Dang, An efficient accelerator for attribute reduction from incomplete data in rough set framework, Pattern Recognit. 44 (2011) 1658-1670.
29. Y.H. Qian, J.Y. Liang, Y.Y. Yao, C.Y. Dang, MGRS: a multi-granulation rough set, Inf. Sci. 180 (2010) 949-970.
30. T. Ruckstieb, C. Osendorfer, P. Smagt, Minimizing data consumption with sequential online feature selection, Int. J. Mach. Learn. Cybern. 4(3) (2013) 235-243.
31. D. Sen, S.K. Pal, Generalized rough sets, entropy and image ambiguity measures, IEEE Trans. Syst. Man Cybern., Part B, Cybern. 39(1) (2009) 117-128.
32. C.E. Shannon, The mathematical theory of communication, Bell Syst. Tech. J. 27(3-4) (1948) 373-423, 623-656.
33. A. Sharma, S. Imoto, S. Miyano, V. Sharma, Null space based feature selection method for gene expression data, Int. J. Mach. Learn. Cybern. 3(4) (2012) 269-276.
34. N. Subrahmanya, Y.C. Shin, A variational Bayesian framework for group feature selection, Int. J. Mach. Learn. Cybern. 4(6) (2013) 609-619.
35. E.C.C. Tang, D.G. Chen, D.S. Yeung, X.Z. Wang, J.W.T. Lee, Attributes reduction using fuzzy rough sets, IEEE Trans. Fuzzy Syst. 16(5) (2008) 1130-1141.
36. X.Z. Wang, E.C.C. Tang, S.Y. Zhao, D.G. Chen, D.S. Yeung, Learning fuzzy rules from fuzzy samples based on rough set technique, Inf. Sci. 177 (2007) 4493-4514.
37. G.Y. Wang, H. Yu, D.C. Yang, Decision table reduction based on conditional information entropy, Chin. J. Comput. 25(11) (2002) 1-8.
38. M.J. Wierman, Measuring uncertainty in rough set theory, Int. J. Gen. Syst. 28(4) (1999) 283-297.
39. W.Z. Wu, J.S. Mi, W.X. Zhang, Generalized fuzzy rough sets, Inf. Sci. 151 (2003) 263-282.
40. W.Z. Wu, W.X. Zhang, Constructive and axiomatic approaches of fuzzy approximation operators, Inf. Sci. 159 (2004) 233-254.
41. Z.X. Xie, Y. Xu, Sparse group LASSO based uncertain feature selection, Int. J. Mach. Learn. Cybern. 5(2) (2014) 201-210.
42. W.H. Xu, Y.F. Liu, T.J. Li, Intuitionistic fuzzy ordered information system, Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 21(3) (2013) 367-390.
43. Y.Y. Yao, Probabilistic rough set approximations, Int. J. Approx. Reason. 49(2) (2008) 255-271.
44. Y.Y. Yao, Interpreting concept learning in cognitive informatics and granular computing, IEEE Trans. Syst. Man Cybern., Part B, Cybern. 39(4) (2009) 855-865.
45. Y.Y. Yao, Y. Zhao, J. Wang, On reduct construction algorithms, Lect. Notes Comput. Sci. 5150 (2008) 100-117.
46. D.S. Yeung, D.G. Chen, E.C.C. Tsang, J.W.T. Lee, X.Z. Wang, On the generalization of fuzzy rough sets, IEEE Trans. Fuzzy Syst. 13(3) (2005) 343-361.
47. S.Y. Zhao, E.C.C. Tsang, D.G. Chen, The model of fuzzy variable precision rough sets, IEEE Trans. Fuzzy Syst. 17(2) (2009) 451-467.
48. S.Y. Zhao, E.C.C. Tsang, X.Z. Wang, Building a rule-based classifier - a fuzzy rough set approach, IEEE Trans. Knowl. Data Eng. 22(5) (2009) 624-638.