Neighborhood effective information ratio for hybrid feature subset evaluation and selection

Neurocomputing

Volumn 99, Issue , 2013, Pages 25-37

  Zhu, Wenzhi ^a   Si, Gangquan ^a   Zhang, Yanbin ^a   Wang, Jingcheng ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Feature evaluation; Feature selection; Hybrid feature; Neighborhood; Neighborhood effective information ratio; Rough set

Indexed keywords

FEATURE EVALUATION; HYBRID FEATURES; NEIGHBORHOOD; NEIGHBORHOOD EFFECTIVE INFORMATION RATIO; ROUGH SET;

ALGORITHMS; DATA MINING;

FEATURE EXTRACTION;

ACCURACY; ARTICLE; CALCULATION; CORRELATION COEFFICIENT; INFORMATION PROCESSING; MATHEMATICAL COMPUTING; NEIGHBORHOOD EFFECTIVE INFORMATION RATIO; PRIORITY JOURNAL; PROBABILITY; STATISTICAL DISTRIBUTION; STATISTICAL MODEL; VALIDATION PROCESS;

EID: 84867878080     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.04.024     Document Type: Article

References (46)

1. Liu H., Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 2005, 17:491-502.
2. Yu L., Liu H. Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 2004, 5:1205-1224.
3. Liu H., Sun J., Liuand Huijie L. Feature selection with dynamic mutual information. Pattern Recognition 2009, 42:1330-1339.
4. Li F., Yin Y. Approaches to knowledge reduction of covering decision systems based on information theory. Inf. Sci. 2009, 179:1694-1704.
5. Jensen R., Shen Q. New approaches to fuzzy-rough feature selection. IEEE Trans. Fuzzy Syst. 2009, 17:824-838.
6. Ng W., Yeung D., Firth M., Tsang E., Wang X. Feature selection using localized generalization error for supervised classification problems using RBFNN. Pattern recognition 2008, 41:3706-3719.
7. Dash M., Liu H. Consistency-based search in feature selection. Artif. Intell. 2003, 151:155-176.
8. Liu H., Motoda H. Feature Selection for Knowledge Discovery and Data Mining 1998, vol. 454. Springer.
9. Kwak N., Choi C. Input feature selection for classification problems. IEEE Trans. Neural Networks 2002, 13:143-159.
10. Somol P., Pudil P., Kittler J. Fast branch & bound algorithms for optimal feature selection. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26:900-912.
11. Chen Y., Miao D., Wang R. A rough set approach to feature selection based on ant colony optimization. Pattern Recognition Lett. 2010, 31:226-233.
12. R. Kohavi, Wrappers for Performance Enhancement and Oblivious Decision Graphs, 1996.
13. Kohavi R., John G. Wrappers for feature subset selection. Artif. Intell. 1997, 97:273-324.
14. Singh S. Multiresolution estimates of classification complexity. IEEE Trans. Pattern Anal. Mach. Intell. 2003, 25:1534-1539.
15. Guyon I., Weston J., Barnhill S., Vapnik V. Gene selection for cancer classification using support vector machines. Mach. Learn. 2002, 46:389-422.
16. Guyon I., Elisseeff A. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3:1157-1182.
17. Mitra P., Murthy C., Pal S. Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 301-312.
18. M. Dash, K. Choi, P. Scheuermann, H. Liu, Feature selection for clustering - a filter solution, in: Second IEEE International Conference on Data Mining, 2002, p. 115.
19. Ho T., Basu M. Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24:289-300.
20. Guan Y., Wang H., Wang Y., Yang F. Attribute reduction and optimal decision rules acquisition for continuous valued information systems. Inf. Sci. 2009, 179:2974-2984.
21. Liu H., Hussain F., Tan C., Dash M. Discretization: an enabling technique. Data Mining Knowl. Discovery 2002, 6:393-423.
22. Ching J., Wong A., Chan K. Class-dependent discretization for inductive learning from continuous and mixed-mode data. IEEE Trans. Pattern Anal. Mach. Intell. 1995, 17:641-651.
23. Polkowski L. Rough Sets: Mathematical Foundations 2002, Physica-Verlag.
24. Wang X., Yang J., Teng X., Xia W., Jensen R. Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Lett. 2007, 28:459-471.
25. Swiniarski R., Skowron A. Rough set methods in feature selection and recognition. Pattern Recognition Lett. 2003, 24:833-849.
26. Qian Y., Liang J., Pedrycz W., Dang C. Positive approximation: an accelerator for attribute reduction in rough set theory. Artif. Intell. 2010, 174:597-618.
27. Qian Y., Liang J., Pedrycz W., Dang C. An efficient accelerator for attribute reduction from incomplete data in rough set framework. Pattern Recognition 2011, 44:1658-1670.
28. Dubois D., Prade H. Rough fuzzy sets and fuzzy rough sets. Int. J. General Syst. 1990, 17:191-209.
29. Jensen R., Shen Q. Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Trans. Knowl. Data Eng. 2004, 16:1457-1471.
30. Hu Q., Yu D. Entropies of fuzzy indiscernibility relation and its operations. Int. J. Uncertainty Fuzziness Knowledge-Based Syst. 2004, 12:575-589.
31. Hu Q., Zhang L., Chen D., Pedrycz W., Yu D. Gaussian kernel based fuzzy rough sets: model, uncertainty measures and applications. Int. J. Approximate Reasoning 2010, 51:453-471.
32. Hu Q., An S., Yu D. Soft fuzzy rough sets for robust feature evaluation and selection. Inf. Sci. 2010, 180:4384-4400.
33. Yeung D., Chen D., Tsang E., Lee J., Xizhao W. On the generalization of fuzzy rough sets. IEEE Trans. Fuzzy Syst. 2005, 13:343-361.
34. Yao Y. Relational interpretations of neighborhood operators and rough set approximation operators. Inf. Sci. 1998, 111:239-259.
35. Wu W., Zhang W. Neighborhood operator systems and approximations. Inf. Sci. 2002, 144:201-217.
36. Hu Q., Yu D., Liu J., Wu C. Neighborhood rough set based heterogeneous feature subset selection. Inf. Sci. 2008, 178:3577-3594.
37. Hu Q., Pedrycz W., Yu D., Lang J. Selecting discrete and continuous features based on neighborhood decision error minimization. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2010, 40:137-150.
38. Hu Q., Che X., Zhang L., Yu D. Feature evaluation and selection based on neighborhood soft margin. Neurocomputing 2010, 73:2114-2124.
39. A. Frank, A. Asuncion, UCI Machine Learning Repository, 2010.
40. C. Hsu, C. Chang, C. Lin, et al., A Practical Guide to Support Vector Classification, 2003.
41. Wang J., Wu X., Zhang C. Support vector machines based on k-means clustering for real-time business intelligence systems. Int. J. Bus. Intell. Data Mining 2005, 1:54-64.
42. Kwak N., Choi C. Input feature selection for classification problems. IEEE Trans. Neural Networks 2002, 13:143-159.
43. M.A. Hall, Correlation-based feature selection for discrete and numeric class machine learning, in: Proceedings of the Seventeenth International Conference on Machine Learning, 2000, pp. 359-366.
44. Kononenko I. Estimating attributes: analysis and extensions of relief. Mach. Learn. 1994, 784:171-182. (ECML-94).
45. Gheyas I., Smith L. Feature subset selection in large dimensionality domains. Pattern Recognition 2010, 43:5-13.
46. R. Quinlan, Data Mining Tools see5 and c5.0, 2004.