A distance measure approach to exploring the rough set boundary region for attribute reduction

IEEE Transactions on Knowledge and Data Engineering

Volumn 22, Issue 3, 2010, Pages 305-317

  Parthaláin, Neil Mac ^a   Shen, Qiang ^a   Jensen, Richard ^a  

^a ABERYSTWYTH UNIVERSITY   (United Kingdom)

Author keywords

Attribute reduction; Boundary region; Classification; Fuzzy sets; Rough sets

Indexed keywords

ATTRIBUTE REDUCTION; BOUNDARY REGION; BOUNDARY REGIONS; CLASSIFICATION ACCURACY; DATA SETS; DEPENDENCY VALUE; DIMENSIONALITY REDUCTION; DISTANCE MEASURE; DISTANCE METRICS; FEATURE SELECTION; LOWER APPROXIMATION; PREDICTION PERFORMANCE; REAL-VALUED DATA; ROUGH SET; RUNTIMES;

DATA VISUALIZATION; FUZZY SETS; INFORMATION USE;

ROUGH SET THEORY;

EID: 76749110434     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2009.119     Document Type: Article

References (38)

1. C. Armanino, R. Leardi, S. Lanteri, and G. Modi, "Chemometric Analysis of Tuscan Olive Oils," Chemometrics and Intelligent Laboratory Systems, vol.5, no.4, pp. 343-354, Apr. 1989.
2. A. Chouchoulas and Q. Shen, "Rough Set-Aided Keyword Reduction for Text Categorisation," Applied Artificial Intelligence, vol.15, no.9, pp. 843-873, 2001.
3. W.W. Cohen, "Fast Effective Rule Induction," Proc. 12th Int'l Conf. Machine Learning, pp. 115-123, 1995.
4. J.S. Deogun, V.V. Raghavan, and H. Sever, "Exploiting Upper Approximation in the Rough Set Methodology," Proc. First Int'l Conf. Knowledge Discovery and Data Mining, pp. 1-10, 1995.
5. D. Dubois and H. Prade, "Putting Rough Sets and Fuzzy Sets Together," Intelligent Decision Support, pp. 203-232, Kluwer Academic Publishers, 1992.
6. Rough-Fuzzy Hybridization: A New Trend in Decision Making, S.K. Pal and A. Skowron, eds. Springer Verlag, 1999.
7. P. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach. Prentice Hall, 1982.
8. A. Hassanien, "Rough Set Approach for Attribute Reduction and Rule Generation: A Case of Patients with Suspected Breast Cancer," J. Am. Soc. Information Science and Technology, vol.55, no.11, pp. 954-962, 2004.
9. A. Hedar, J. Wang, and M. Fukushima, "Tabu Search for Attribute Reduction in Rough Set Theory," Technical Report 2006-2008, Dept. of Applied Mathematics and Physics, Kyoto Univ., 2006.
10. M. Inuiguchi and T. Tanino, New Fuzzy-Rough Sets Based on Certainty Qualification, Rough-Neural Computing: Techniques for Computing with Words, S.K. Pal, L. Polkowski, and A. Skowron, eds. Springer-Verlag, 2003.
11. M. Inuiguchi and M. Tsurumi, "Measures Based on Upper Approximations of Rough Sets for Analysis of Attribute Importance and Interaction," Int'l J. Innovative Computing, Information and Control, vol.2, no.1, pp. 1-12, 2006.
12. R. Jensen and Q. Shen, "Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches," IEEE Trans. Knowledge and Data Eng., vol.16, no.12, pp. 1457-1471, Dec. 2004.
13. H.R. Li and W.X. Zhang, "Applying Indiscernibility Attribute Sets to Knowledge Reduction," Lecture Notes in Artificial Intelligence, pp. 816-821, Springer, 2005.
14. K. Li, Y. Liu, "Rough Set Based Attribute Reduction Approach in Data Mining," Proc. 2002 Int'l Conf. Machine Learning and Cybernetics, vol.1, pp. 60-63, 2002.
15. N. Mac Parthaláin, R. Jensen, and Q. Shen, "Fuzzy Entropy-Assisted Fuzzy-Rough Feature Selection," Proc. 15th Int'l Conf. Fuzzy Systems (FUZZ-IEEE '06) 2006.
16. N. Mac Parthaláin, R. Jensen, and Q. Shen, "Distance Measure Assisted Rough Set Feature Selection," Proc. 16th Int'l Conf. Fuzzy Systems (FUZZ-IEEE '07), pp. 1084-1089, 2007.
17. N. Mac Parthaláin and Q. Shen, "Exploring the Boundary Region of Tolerance Rough Sets for Feature Selection," Pattern Recognition, vol.42, pp. 655-667, http://www.sciencedirect.com/ science/article/B6V14- 4TDC09M-1/2/9735ab90392246f032a2632 eda77ae0e, May 2009.
18. M. Modrzejewski, "Feature Selection Using Rough Sets Theory," Proc. European Conf. Machine Learning, P.B. Brazdil, ed., pp. 213-226, 1993.
19. D.J. Newman, S. Hettich, C.L. Blake, and C.J. Merz, "UCI Repository of Machine Learning Databases," Dept. of Information and Computer Science, Univ. of California, http://www.ics. uci.edu/mlearn/MLRepository.html, 1998.
20. R. Nie and J. Yue, "An Attribute Reduction Method Based on Rough Set and SVM and with Application in Oil-Gas Prediction," Proc. Sixth IEEE/ACIS Int'l Conf. Computer and Information Science (ICIS '07), pp. 502-506, 2007.
21. S.H. Nguyen and A. Skowron, "Searching for Relational Patterns in Data," Proc. First European Symp. Principles of Data Mining and Knowledge Discovery, pp. 265-276, 1997.
22. S. Piramuthu, "The Hausdorff Distance Measure for Feature Selection in Learning Applications," Proc. 32nd Ann. Hawaii Int'l Conf. System Sciences, vol.6, 1999.
23. S.K. Pal and P. Mitra, "Case Generation Using Rough Sets with Fuzzy Representation," IEEE Trans. Knowledge and Data Eng., vol.16, no.3, pp. 292-300, Mar. 2004.
24. Z. Pawlak, "Rough Sets," Int'l J. Computer and Information Science, vol.11, pp. 341-356, 1982.
25. J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
26. W. Rucklidge, Efficient Visual Recognition Using the Hausdorff Distance. Springer, 1996.
27. B. Sendov, "Hausdorff Distance and Image Processing," Russian Math Surveys, vol.59, no.2, pp. 319-328, 2004.
28. Q. Shen and R. Jensen, "Selecting Informative Features with Fuzzy-Rough Sets and Its Application for Complex Systems Monitoring," Pattern Recognition, vol.37, no.7, pp. 1351-1363, 2004.
29. A. Skowron and J. Stepaniuk, "Tolerance Approximation Spaces," Fundamenta Informaticae, vol.27, pp. 245-253, 1996.
30. D. Slezak, "Various Approaches to Reasoning with Frequency Based Decision Reducts: A Survey," Rough Set Methods and Applications, L. Polkowski, S. Tsumoto, T.Y. Lin, eds., pp 235-285, Physica-Verlag, 2000.
31. Intelligent Decision Support, R. Slowinski, ed. Kluwer Academic Publishers, 1992.
32. E.P.M. de Sousa, C. Traina, A.J.M. Traina, L. Wu, and C. Faloutsos, "A Fast and Effective Method to Find Correlations among Attributes in Databases," Data Mining and Knowledge Discovery, vol.14, pp. 367-407, 2007.
33. R.W. Swiniarski and A. Skowron, "Rough Set Methods in Feature Selection and Recognition," Pattern Recognition Letters, vol.24, no.6, pp. 833-849, 2003.
34. I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools with Java Implementations. Morgan Kaufmann, 2000.
35. I.H. Witten and E. Frank, "Generating Accurate Rule Sets without Global Optimization," Proc. 15th Int'l Conf. Machine Learning, 1998.
36. Y. Yao, "A Comparative Study of Fuzzy Sets and Rough Sets," Information Sciences, vol.109, pp. 21-47, 1998.
37. N. Zhong, J. Dong, and S. Ohsuga, "Using Rough Sets with Heuristics for Feature Selection," J. Intelligent Information Systems, vol.16, no.3, pp. 199-214, 2001.
38. W. Ziarko, "Variable Precision Rough Set Model," J. Computer and System Sciences, vol.46, no.1, pp. 39-59, 1993.