1 |
Z. Pawlak, "Rough sets," International Journal of Computer & Information Sciences, vol. 11, no. 5, pp. 341-356, 1982.
DOI
|
2 |
R. Jensen and Q. Shen, "New approaches to fuzzy-rough feature selection," IEEE Transactions on fuzzy Systems, vol. 17, no. 4, pp. 824-838, 2009.
DOI
|
3 |
Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data. Dordrecht: Kluwer Academic Publishers, 1991
|
4 |
M. Kryszkiewicz, "Certain, generalized decision, and membership distribution reducts versus functional dependencies in incomplete systems," in Rough Sets and Intelligent Systems Paradigms. Heidelberg: Springer, 2007, pp. 162-174.
|
5 |
M. Li, S. Deng, S. Feng, and J. Fan, "Fast assignment reduction in inconsistent incomplete decision systems," Journal of Systems Engineering and Electronics, vo. 25, no. 1, pp. 83-94, 2014.
DOI
|
6 |
W. X. Zhang, J. S. Mi, and W. Z. Wu, "Approaches to knowledge reductions in inconsistent systems," International Journal of Intelligent Systems, vol. 18, no. 9, pp. 989-1000, 2003.
DOI
|
7 |
M. Li, C. Shang, S. Feng, and J. Fan, "Quick attribute reduction in inconsistent decision tables," Information Sciences, vol. 254, pp. 155-180, 2014.
DOI
|
8 |
M. Inuiguchi, Y. Yoshioka, and Y. Kusunoki, "Variable-precision dominance-based rough set approach and attribute reduction," International Journal of Approximate Reasoning, vol. 50, no. 8, pp. 1199-1214, 2009.
DOI
|
9 |
J. Y. Wang and J. Zhou, "Research of reduct features in the variable precision rough set model," Neurocomputing, vol. 72, no. 10-12, pp. 2643-2648, 2009.
DOI
|
10 |
R. R. Yager, J. Kacprzyk, and M. Fedrizzi, Advances in the Dempster-Shafer Theory of Evidence. New York, NY: John Wiley & Sons Inc., 1994.
|
11 |
D. Ye and Z. Chen, "A new type of attribute reduction for inconsistent decision tables and its computation," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 18, no. 2, pp. 209-222, 2010.
DOI
|
12 |
X. Jia, W. Liao, Z. Tang, and L. Shang, "Minimum cost attribute reduction in decision-theoretic rough set models," Information Sciences, vol. 219, pp. 151-167, 2013.
DOI
|
13 |
J. Li, H. Zhao, and W. Zhu, "Fast randomized algorithm with restart strategy for minimal test cost feature selection," International Journal of Machine Learning and Cybernetics, vol. 6, no. 3, pp. 435-442, 2015.
DOI
|
14 |
Q. Hu, Z. Xie, and D. Yu, "Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation," Pattern Recognition, vol. 40, no. 12, pp. 3509-3521, 2007.
DOI
|
15 |
M. Li, S. Deng, L. Wang, S. Feng, and J. Fan, "Hierarchical clustering algorithm for categorical data using a probabilistic rough set model," Knowledge-Based Systems, vol. 65, pp. 60-71, 2014.
DOI
|
16 |
D. Q. Miao and G. R. Hu, "A heuristic algorithm for reduction of knowledge," Journal of Computer Research and Development, vol. 36, no. 6, pp. 681-684, 1999.
|
17 |
G. Y. Wang, H. Yu, and D. C. Yang, "Decision table reduction based on conditional information entropy," Chinese Journal of Computers, vol. 25, no. 7, pp. 759-766, 2002.
DOI
|
18 |
L. L. Minku, A. P. White, and X. Yao, "The impact of diversity on online ensemble learning in the presence of concept drift," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 5, pp. 730-742, 2010.
DOI
|
19 |
R. Jensen and Q. Shen, "Fuzzy-rough attribute reduction with application to web categorization," Fuzzy Sets and Systems, vol. 141, no. 3, pp. 469-485, 2004.
DOI
|
20 |
Y. Qian, J. Liang, W. Pedrycz, and C. Dang, "Positive approximation: an accelerator for attribute reduction in rough set theory," Artificial Intelligence, vol. 174, no. 9-10, pp. 597-618, 2010.
DOI
|
21 |
J. Ross Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann Publishers, 2014.
|
22 |
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge, UK: Cambridge University Press, 2000.
|
23 |
V. Garcia, J. S. Sanchez, and R. A. Mollineda, "On the effectiveness of preprocessing methods when dealing with different levels of class imbalance," Knowledge-Based Systems, vol. 25, no. 1, pp. 13-21, 2012.
DOI
|
24 |
Z. H. Zhou, Ensemble Methods: Foundations and Algorithms. Boca Raton, FL: Taylor & Francis, 2012.
|