Attribute reduction for set-valued data based on prediction label

References

• Badri, P., and M. Sojoodi. 2022. “LMI-based Robust Stability and Stabilization Analysis of Fractional-order Interval Systems with Time-varying Delay.” International Journal of General Systems 51 (1): 1–26. doi:10.1080/03081079.2021.1993847.
   Web of Science ®Google Scholar
• Cai, J., J. W. Luo, S. L. Wang, and S. Yang. 2018. “Feature Selection in Machine Learning: A New Perspective.” Neurocomputing 300: 70–79. doi:10.1016/j.neucom.2017.11.077.
   Web of Science ®Google Scholar
• Chen, H. M., T. R. Li, X. Fan, and C. Luo. 2019. “Feature Selection for Imbalanced Data Based on Neighborhood Rough Sets.” Information Sciences 483: 1–20. doi:10.1016/j.ins.2019.01.041.
   Web of Science ®Google Scholar
• Chen, L. J., S. M. Liao, N. X. Xie, Z. W. Li, G. Q. Zhang, and C. F. Wen. 2020. “Measures of Uncertainty for an Incomplete Set-valued Information System with the Optimal Selection of Subsystems: Gaussian Kernel Method.” IEEE Access 8: 212022–212035. doi:10.1109/Access.6287639.
   Google Scholar
• Dai, J. H., and H. W. Tian. 2013. “Entropy Measures and Granularity Measures for Set-valued Information Systems.” Information Sciences 240: 72–82. doi:10.1016/j.ins.2013.03.045.
   Web of Science ®Google Scholar
• Dai, J. H., and H. W. Tian. 2013. “Fuzzy Rough Set Model for Set-valued Data.” Fuzzy Sets and Systems229: 54–68. doi:10.1016/j.fss.2013.03.005.
   Web of Science ®Google Scholar
• Demšar, J. 2006. “Statistical Comparisons of Classifiers Over Multiple Data Sets.” The Journal of Machine Learning Research 7: 1–30. doi:10.5555/1248547.1248548.
   Web of Science ®Google Scholar
• Friedman, M. 1940. “A Comparison of Alternative Tests of Significance for the Problem of M Rankings.” The Annals of Mathematical Statistics 11 (1): 86–92. doi:10.1214/aoms/1177731944.
   Google Scholar
• Ge, Y. X., D. P. Chen, and H. S. Li. 2020. “Mutual Mean-teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification.” ArXiv Preprint ArXiv : 2001.01526.
   Google Scholar
• Grzymala-Busse, J. W. 1991. “On the Unknown Attribute Values in Learning from Examples.” Methodologies for Intelligent Systems: 6th International Symposium, ISMIS'91 Charlotte, NC, USA, 16C19, 1991 Proceedings 6. Berlin, Heidelberg: Springer.
   Google Scholar
• Grzymala-Busse, J. W. 2005. “Rough Set Theory with Applications to Data Mining.” In Real World Applications of Computational Intelligence, Berlin, Heidelberg: Springer.
   Google Scholar
• Grzymala-Busse, J. W., and M. Hu. 2000. “A Comparison of Several Approaches to Missing Attribute Values in Data Mining.” Rough Sets and Current Trends in Computing: Second International Conference, RSCTC 2000 Banff, Canada, October 16C19, 2000 Revised Papers 2. Berlin, Heidelberg: Springer.
   Google Scholar
• Guan, Y. Y., and H. K. Wang. 2006. “Set-valued Information Systems.” Information Sciences 176 (17): 2507–2525. doi:10.1016/j.ins.2005.12.007.
   Web of Science ®Google Scholar
• Hu, J., S. Y. Huang, and R. Shao. 2018. “Attribute Reduction of Set-valued Decision Information System.” Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations: 17th International Conference, IPMU 2018, Cdiz, Spain, 11–15, 2018, Proceedings, Part II 17. Springer International Publishing.
   Google Scholar
• Hu, Q. H., W. Pedrycz, D. R. Yu, and J. Lang. 2009. “Selecting Discrete and Continuous Features Based on Neighborhood Decision Error Minimization.” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40 (1): 137–150. doi:10.1109/TSMCB.2009.2024166.
   PubMed Web of Science ®Google Scholar
• Hu, Z. J., Z. Y. Yang, X. F. Hu, and R. Nevatia. 2021. “Simple: Similar Pseudo Label Exploitation for Semi-supervised Classification.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
   Google Scholar
• Huang, Y. Y., T. R. Li, C. Luo, H. Fujita, and S. J. Horng. 2017. “Dynamic Variable Precision Rough Set Approach for Probabilistic Set-valued Information Systems.” Knowledge-Based Systems 122: 131–147. doi:10.1016/j.knosys.2017.02.002.
   Web of Science ®Google Scholar
• Jayasuruthi, L., A. Shalini, and V. V. Kumar. 2018. “Application of Rough Set Theory in Data Mining Market Analysis Using Rough Sets Data Explorer.” Journal of Computational and Theoretical Nanoscience 15 (6): 2126–2130. doi:10.1166/jctn.2018.7420.
   Google Scholar
• Jia, X. Y., Y. Rao, L. Shang, and T. J. Li. 2020. “Similarity-based Attribute Reduction in Rough Set Theory: A Clustering Perspective.” International Journal of Machine Learning and Cybernetics 11 (5): 1047–1060. doi:10.1007/s13042-019-00959-w.
   Web of Science ®Google Scholar
• Jothi, G., H. H. Inbarani, A. T. Azar, and K. R. Devi. 2019. “Rough Set Theory with Jaya Optimization for Acute Lymphoblastic Leukemia Classification.” Neural Computing and Applications 31 (9): 5175–5194. doi:10.1007/s00521-018-3359-7.
   Web of Science ®Google Scholar
• Kryszkiewicz, M. 1998. “Rough Set Approach to Incomplete Information Systems.” Information Sciences112 (1-4): 39–49. doi:10.1016/S0020-0255(98)10019-1.
   Web of Science ®Google Scholar
• Kryszkiewicz, M. 1999. “Rules in Incomplete Information Systems.” Information Sciences 113 (3-4): 271–292. doi:10.1016/S0020-0255(98)10065-8.
   Web of Science ®Google Scholar
• Li, J. Z., X. B. Yang, X. N. Song, J. H. Li, P. X. Wang, and D. J. Yu. 2019. “Neighborhood Attribute Reduction: A Multi-criterion Approach.” International Journal of Machine Learning and Cybernetics10 (4): 731–742. doi:10.1007/s13042-017-0758-5.
   Web of Science ®Google Scholar
• Liang, J., R. He, Z. N. Sun, and T. N. Tan. 2019. “Exploring Uncertainty in Pseudo-label Guided Unsupervised Domain Adaptation.” Pattern Recognition 96: 106996. doi:10.1016/j.patcog.2019.106996.
   Google Scholar
• Lin, S. 1995. Generalized Metric Spaces and Mappings. Beijing: Chinese Scientific Publishers.
   Google Scholar
• Luo, C., T. R. Li, H. M. Chen, and L. X. Lu. 2015. “Fast Algorithms for Computing Rough Approximations in Set-valued Decision Systems While Updating Criteria Values.” Information Sciences299: 221–242. doi:10.1016/j.ins.2014.12.029.
   Web of Science ®Google Scholar
• Mohabey, A., and A. K. Ray. 2000. “Rough Set Theory Based Segmentation of Color Images.” PeachFuzz 2000. 19th International Conference of the North American Fuzzy Information Processing Society-NAFIPS (Cat. No. 00TH8500). IEEE.
   Google Scholar
• Oh, Y., D. J. Kim, and I. S. Kweon. 2021. “Distribution-aware Semantics-oriented Pseudo-label for Imbalanced Semi-supervised Learning.” ArXiv Preprint ArXiv : 2106.05682.
   Google Scholar
• Pawlak, Z. 1982. “Rough Sets.” International Journal of Computer and Information Sciences 11 (5): 341–356. doi:10.1007/BF01001956.
   Web of Science ®Google Scholar
• Pawlak, Z. 1991. Rough Sets: Theoretical Aspects of Reasoning about Data. Dordrecht: Kluwer Academic Publishers.
   Google Scholar
• Prasad, M., S. Tripathi, and K. Dahal. 2020. “An Efficient Feature Selection Based Bayesian and Rough Set Approach for Intrusion Detection.” Applied Soft Computing 87: 105980. doi:10.1016/j.asoc.2019.105980.
   Web of Science ®Google Scholar
• Qian, Y. H., C. Y. Dang, J. Y. Liang, and D. W. Tang. 2009. “Set-valued Ordered Information Systems.” Information Sciences 179 (16): 2809–2832. doi:10.1016/j.ins.2009.04.007.
   Web of Science ®Google Scholar
• Qian, Y. H., X. Y. Liang, Q. Wang, J. Y. Liang, B. Liu, A. Skowron, Y. Y. Yao, J. M. Ma, and C. Y. Dang. 2018. “Local Rough Set: A Solution to Rough Data Analysis in Big Data.” International Journal of Approximate Reasoning 97: 38–63. doi:10.1016/j.ijar.2018.01.008.
   Web of Science ®Google Scholar
• Roy, P., S. Goswami, S. Chakraborty, A. T. Azar, and N. Dey. 2014. “Image Segmentation Using Rough Set Theory: A Review.” International Journal of Rough Sets and Data Analysis 1 (2): 62–74. doi:10.4018/IJRSDA.
   Google Scholar
• Singh, S., S. Shreevastava, T. Som, and G. Somani. 2020. “A Fuzzy Similarity-based Rough Set Approach for Attribute Selection in Set-valued Information Systems.” Soft Computing 24 (6): 4675–4691. doi:10.1007/s00500-019-04228-4.
   Web of Science ®Google Scholar
• Wakulicz-Deja, A., and P. Peszek. 2003. “Applying Rough Set Theory to Multi Stage Medical Diagnosing.” Fundamenta Informaticae 54 (4): 387–408. doi:10.5555/873901.873905.
   Web of Science ®Google Scholar
• Wang, C. Z., Q. H. Hu, X. Z. Wang, D. G. Chen, Y. H. Qian, and Z. Dong. 2017. “Feature Selection Based on Neighborhood Discrimination Index.” IEEE Transactions on Neural Networks and Learning Systems 29 (7): 2986–2999. doi:10.1109/TNNLS.2017.2710422.
   PubMed Web of Science ®Google Scholar
• Yang, X. B., S. C. Liang, H. L. Yu, S. Gao, and Y. H. Qian. 2019. “Pseudo-label Neighborhood Rough Set: Measures and Attribute Reductions.” International Journal of Approximate Reasoning 105: 112–129. doi:10.1016/j.ijar.2018.11.010.
   Web of Science ®Google Scholar
• Yao, Y. Y. 2001. “Information Granulation and Rough Set Approximation.” International Journal of Intelligent Systems 16 (1): 87–104. doi:10.1002/1098-111X(200101)16:1<>1.0.CO;2-B.
   Web of Science ®Google Scholar
• Yao, Y. Y., and Q. Liu. 1999. “A Generalized Decision Logic in Interval-set-valued Information Tables.” New Directions in Rough Sets, Data Mining, and Granular-soft Computing: 7th International Workshop, RSFDGrC'99, Yamaguchi, Japan, November 9-11, 1999. Proceedings 7. Berlin, Heidelberg: Springer.
   Google Scholar
• Zhang, Y. X., and Z. Z. Chen. 2021. “Information Entropy Based Attribute Reduction for Incomplete Set-valued Data.” IEEE Access 10: 8864–8882. doi:10.1109/ACCESS.2021.3138961.
   Web of Science ®Google Scholar
• Zhang, Q. L., Y. Y. Chen, G. Q. Zhang, Z. W. Li, L. J. Chen, and C. F. Wen. 2021. “New Uncertainty Measurement for Categorical Data Based on Fuzzy Information Structures: An Application in Attribute Reduction.” Information Sciences 580: 541–577. doi:10.1016/j.ins.2021.08.089.
   Web of Science ®Google Scholar
• Zhang, Q. L., and L. L. Li. 2022. “Attribute Reduction for Set-valued Data Based on D-S Evidence Theory.” International Journal of General Systems 51 (8): 822–861. doi:10.1080/03081079.2022.2086241.
   Web of Science ®Google Scholar