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Articles

A weighted complement-entropy system based on tri-level granular structures

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Pages 872-905 | Received 22 Aug 2019, Accepted 02 Aug 2020, Published online: 27 Aug 2020

References

  • Azam, N., Y. Zhang, and J. T. Yao. 2017. “Evaluation Functions and Decision Conditions of Three-Way Decisions with Game-Theoretic Rough Sets.” European Journal of Operational Research 261 (2): 704–714.
  • Bishop, C. M. 2006. Pattern Recognition and Machine Learning. Singapore: Springer.
  • Boixader, D., and J. Recasens. 2018. “Reduction of Attributes in Averaged Similarities.” Information Sciences 426: 117–130.
  • Cabitza, F., D. Ciucci, and A. Locoro. 2016. “Exploiting Collective Knowledge with Three-Way Decision Theory: Cases From the Questionnaire-Based Research.” International Journal of Approximate Reasoning 83: 356–370.
  • Calvanese, D., M. Dumas, U. Laurson, F. M. Maggi, M. Montali, and I. Teinemaa. 2018. “Semantics, Analysis and Simplification of DMN Decision Tables.” Information Systems 78: 112–125.
  • Chen, Y. M., Y. Xue, Y. Ma, and F. F. Xu. 2017. “Measures of Uncertainty for Neighborhood Rough Sets.” Knowledge-Based Systems 120: 226–235.
  • Cornejo, M. E., J. Medina, and E. Ramirez-Poussa. 2017. “Attribute and Size Reduction Mechanisms in Multi-Adjoint Concept Lattices.” Journal of Computational and Applied Mathematics 318: 388–402.
  • Fan, T. F., C. J. Liau, and D. R. Liu. 2011. “A Relational Perspective of Attribute Reduction in Rough Set-Based Data Analysis.” European Journal of Operational Research 213 (1): 270–278.
  • Ge, H., L. S. Li, Y. Xu, and C. J. Yang. 2017. “Quick General Reduction Algorithms for Inconsistent Decision Tables.” International Journal of Approximate Reasoning 82: 56–80.
  • He, R. H., H. Li, B. Zhang, and M. Chen. 2020. “The Multi-Level Warehouse Layout Problem with Uncertain Information: Uncertainty Theory Method.” International Journal of General Systems 49 (5): 497–520.
  • Honko, P. 2016. “Attribute Reduction: A Horizontal Data Decomposition Approach.” Soft Computing 20 (3): 951–966.
  • Honko, P. 2018. “Horizontal Decomposition of Data Table for Finding One Reduct.” International Journal of General Systems 47 (3): 208–243.
  • Hu, Q. H., D. R. Yu, Z. X. Xie, and J. F. Liu. 2006. “Fuzzy Probabilistic Approximation Spaces and Their Information Measures.” IEEE Transactions on Fuzzy Systems 14 (2): 191–201.
  • Jia, X. Y., L. Shang, B. Zhou, and Y. Y. Yao. 2016. “Generalized Attribute Reduct in Rough Set Theory.” Knowledge-Based Systems 91: 204–218.
  • Jiang, F., Y. F. Sui, and L. Zhou. 2015. “A Relative Decision Entropy-Based Feature Selection Approach.” Pattern Recognition 48 (7): 2151–2163.
  • Joshi, R., and S. Kumar. 2016. “(R,S)-norm Information Measure and a Relation Between Coding and Questionnaire Theory.” Open Systems & Information Dynamics 23 (03): 1650015.
  • Konecny, J. 2017. “On Attribute Reduction in Concept Lattices: Methods Based on Discernibility Matrix are Outperformed by Basic Clarification and Reduction.” Information Sciences 415–416: 199–212.
  • Lang, G. M., D. Q. Miao, and H. Fujita. 2020. “Three-Way Group Conflict Analysis Based on Pythagorean Fuzzy Set Theory.” IEEE Transactions on Fuzzy Systems 28 (3): 447–461.
  • Liang, J. Y., K. S. Chin, C. Y. Dang, and R. C. M. Yam. 2002. “A New Method for Measuring Uncertainty and Fuzziness in Rough Set Theory.” International Journal of General Systems 31 (4): 331–342.
  • Liang, J. Y., and Z. Z. Shi. 2004. “The Information Entropy, Rough Entropy and Knowledge Granulation in Rough Set Theory.” International Journal of Uncertainty, Fuzziness and Knowledge-based Systems 12 (1): 37–46.
  • Liang, J. Y., Z. Z. Shi, D. Y. Li, and M. J. Wierman. 2006. “Information Entropy, Rough Entropy and Knowledge Granulation in Incomplete Information Systems.” International Journal of General Systems 35 (6): 641–654.
  • Liu, G. L., Z. Hua, and J. Y. Zou. 2018. “Local Attribute Reductions for Decision Tables.” Information Sciences 422: 204–217.
  • Liu, X. F., Z. W. Li, G. Q. Zhang, and N. X. Xie. 2019. “Measures of Uncertainty for a Distributed Fully Fuzzy Information System.” International Journal of General Systems 48 (6): 625–655.
  • Ma, X. A., G. Y. Wang, H. Yu, and T. R. Li. 2014. “Decision Region Distribution Preservation Reduction in Decision-theoretic Rough Set Model.” Information Sciences 278: 614–640.
  • Ma, X. A., and Y. Y. Yao. 2018. “Three-Way Decision Perspectives on Class-Specific Attribute Reducts.” Information Sciences 450: 227–245.
  • Miao, D. Q. 1997. “Rough Set Theory and its Application in Machine Learing.” PhD thesis, Institute of Automation, The Chinese Academy of Sciences, Beijing (in Chinese).
  • Miao, D. Q., Y. Zhao, Y. Y. Yao, H. X. Li, and F. F. Xu. 2009. “Relative Reducts in Consistent and Inconsistent Decision Tables of the Pawlak Rough Set Model.” Information Sciences 179 (24): 4140–4150.
  • Pawlak, Z. 1991. Rough Sets: Theoretical Aspect of Reasoning About Data. Dordrecht: Kluwer Academic Publishers.
  • Qian, Y. H., and J. Y. Liang. 2006. “Combination Entropy and Combination Granulation in Incomplete Information System.” Lecture Notes in Artificial Intelligence 4062: 184–190.
  • Qian, Y. H., and J. Y. Liang. 2008. “combination Entropy and Combination Granulation in Rough Set Theory.” International Journal of Uncertainty, Fuzziness and Knowledge-based Systems 16 (2): 179–193.
  • Raza, M. S., and U. Qamar. 2017. “Redefining Core Preliminary Concepts of Classic Rough Set Theory for Feature Selection.” Engineering Applications of Artificial Intelligence 65: 375–387.
  • Saha, I., J. P. Sarkar, and U. Maulik. 2019. “Integrated Rough Fuzzy Clustering for Categorical Data Analysis.” Fuzzy Sets and Systems 361: 1–32.
  • Shannon, C. E. 1948. “A Mathematical Theory of Communication.” The Bell System Technical Journal 27: 379–423.
  • Shiraz, R. K., H. Fukuyama, M. Tavana, and D. D. Caprio. 2016. “An Integrated Data Envelopment Analysis and Free Disposal Hull Framework for Cost-Efficiency Measurement Using Rough Sets.” Applied Soft Computing 46: 204–219.
  • Slezak, D. 2002. “Approximate Entropy Reducts.” Fundamenta Informaticae 53: 365–390.
  • Slezak, D., and W. Ziarko. 2005. “The Investigation of the Bayesian Rough Set Model.” International Journal of Approximate Reasoning 40 (1-2): 81–91.
  • Słowiński, R., S. Greco, and B. Matarazzo. 2002. “Rough Set Analysis of Preference-Ordered Data.” Lecture Notes in Computer Science 2475: 44–59.
  • Wang, G. Y., X. A. Ma, and H. Yu. 2015. “Monotonic Uncertainty Measures for Attribute Reduction in Probabilistic Rough Set Model.” International Journal of Approximate Reasoning 59: 41–67.
  • Wang, J., L. Y. Tang, X. Y. Zhang, and Y. Y. Luo. 2017. “Three-Way Weighted Combination-Entropies Based on Three-Layer Granular Structures.” Applied Mathematics and Nonlinear Sciences 2 (2): 329–340.
  • Wang, G. Y., J. Zhao, J. J. An, and Y. Wu. 2005. “A Comparative Study of Algebra Viewpoint and Information Viewpoint in Attribute Reduction.” Fundamenta Informaticae 68 (3): 289–301.
  • Xu, W. H., and Y. T. Guo. 2016. “Generalized Multigranulation Double-Quantitative Decision-Theoretic Rough Set.” Knowledge-Based Systems 105 (1): 190–205.
  • Yao, Y. Y. 2012. “An Outline of a Theory of Three-Way Decisions.” Lecture Notes in Computer Science 7413: 1–17.
  • Yao, Y. Y. 2016. “Three-Way Decisions and Cognitive Computing.” Cognitive Computation 8 (4): 543–554.
  • Yao, Y. Y. 2018. “Three-Way Decision and Granular Computing.” International Journal of Approximate Reasoning 103: 107–123.
  • Yao, Y. Y., and X. Y. Zhang. 2017. “Class-Specific Attribute Reducts in Rough Set Theory.” Information Sciences 418–419: 601–618.
  • Yu, H., X. C. Wang, G. Y. Wang, and X. H. Zeng. 2020. “An Active Three-Way Clustering Method Via Low-Rank Matrices for Multi-View Data.” Information Sciences 507: 823–839.
  • Yuan, Z., X. Y. Zhang, and S. Feng. 2018. “Hybrid Data-Driven Outlier Detection Based on Neighborhood Information Entropy and Its Developmental Measures.” Expert Systems with Applications 112: 243–257.
  • Yue, X. D., Y. F. Chen, D. Q. Miao, and H. Fujita. 2020. “Fuzzy Neighborhood Covering for Three-Way Classification.” Information Sciences 507: 795–808.
  • Zhang, X. Y., and D. Q. Miao. 2016. “Quantitative/Qualitative Region-Change Uncertainty/Certainty in Attribute Reduction: Comparative Region-Change Analyses Based on Granular Computing.” Information Sciences 334–335: 174–204.
  • Zhang, X. Y., and D. Q. Miao. 2017. “Three-Layer Granular Structures and Three-Way Informational Measures of a Decision Table.” Information Sciences 412–413: 67–86.
  • Zhang, X. Y., X. Tang, J. L. Yang, and Z. Y. Lv. 2020. “Quantitative Three-Way Class-specific Attribute Reducts Based on Region Preservations.” International Journal of Approximate Reasoning 117: 96–121.
  • Zhang, X. Y., J. L. Yang, and L. Y. Tang. 2020. “Three-Way Class-Specific Attribute Reducts From the Information Viewpoint.” Information Sciences 507: 840–872.

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