Knowledge-based system for three-way decision-making under uncertainty

[1]  Weihua Xu,et al.  Two-Way Concept-Cognitive Learning Method: A Fuzzy-Based Progressive Learning , 2023, IEEE Transactions on Fuzzy Systems.

[2]  Mengjun Hu.  Modeling relationships in three-way conflict analysis with subsethood measures , 2022, Knowl. Based Syst..

[3]  M. Wu,et al.  Multi-granularity stock prediction with sequential three-way decisions , 2022, Inf. Sci..

[4]  A. Rajasekar,et al.  An evidence-based credit evaluation ensemble framework for online retail SMEs , 2022, Knowledge and Information Systems.

[5]  Ramisetty Kavya,et al.  Interpretable systems based on evidential prospect theory for decision-making , 2022, Applied Intelligence.

[6]  Manipushpak Mitra,et al.  Expanding and confusing space of alternatives: A case for lexicographic preferences , 2022, Journal of Mathematical Psychology.

[7]  S. Effati,et al.  A new approach for fuzzy classification by a multiple-attribute decision-making model , 2022, Soft Computing.

[8]  Chih-Fong Tsai,et al.  Empirical comparison of supervised learning techniques for missing value imputation , 2022, Knowledge and Information Systems.

[9]  Yiyu Yao.  Symbols-Meaning-Value (SMV) space as a basis for a conceptual model of data science , 2022, Int. J. Approx. Reason..

[10]  Chunmao Jiang,et al.  Three-way decision based on confidence level change in rough set , 2022, Int. J. Approx. Reason..

[11]  Weihua Xu,et al.  An incremental learning mechanism for object classification based on progressive fuzzy three-way concept , 2021, Inf. Sci..

[12]  Huaxiong Li,et al.  A Prospect Theory-Based Three-Way Conflict Analysis Approach for Agent Evaluation , 2021, 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[13]  Fuyuan Xiao,et al.  A fuzzy preference-based Dempster-Shafer evidence theory for decision fusion , 2021, Inf. Sci..

[14]  Zeshui Xu,et al.  Three-way multi-attribute decision making under hesitant fuzzy environments , 2021, Inf. Sci..

[15]  Fuyuan Xiao,et al.  An improved approach to generate generalized basic probability assignment based on fuzzy sets in the open world and its application in multi-source information fusion , 2020, Appl. Intell..

[16]  Huchang Liao,et al.  A Prospect Theory-Based Evidential Reasoning Approach for Multi-expert Multi-criteria Decision-Making with Uncertainty Considering the Psychological Cognition of Experts , 2020, Int. J. Fuzzy Syst..

[17]  Ran Fang,et al.  A Prospect Theory-Based Evidential Reasoning Approach for Multi-expert Multi-criteria Decision-Making with Uncertainty Considering the Psychological Cognition of Experts , 2020, International Journal of Fuzzy Systems.

[18]  Fuyuan Xiao,et al.  FR–KDE: A Hybrid Fuzzy Rule-Based Information Fusion Method with its Application in Biomedical Classification , 2020, International Journal of Fuzzy Systems.

[19]  Yongchuan Tang,et al.  A new base basic probability assignment approach for conflict data fusion in the evidence theory , 2020, Appl. Intell..

[20]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[21]  Guangming Lang,et al.  Three-way conflict analysis: A unification of models based on rough sets and formal concept analysis , 2020, Knowl. Based Syst..

[22]  Yongchuan Tang,et al.  A Novel Failure Mode and Effects Analysis Model Using Triangular Distribution-Based Basic Probability Assignment in the Evidence Theory , 2020, IEEE Access.

[23]  José A. Pino,et al.  Applying Dempster-Shafer theory for developing a flexible, accurate and interpretable classifier , 2020, Expert Syst. Appl..

[24]  Zeshui Xu,et al.  A decision-making framework based on prospect theory with probabilistic linguistic term sets , 2020, J. Oper. Res. Soc..

[25]  Bing Huang,et al.  Sequential three-way decision based on multi-granular autoencoder features , 2020, Inf. Sci..

[26]  Huchang Liao,et al.  Generalised probabilistic linguistic evidential reasoning approach for multi-criteria decision-making under uncertainty , 2019, J. Oper. Res. Soc..

[27]  S. Meysam Mousavi,et al.  A new last aggregation method of multi-attributes group decision making based on concepts of TODIM, WASPAS and TOPSIS under interval-valued intuitionistic fuzzy uncertainty , 2019, Knowledge and Information Systems.

[28]  Horst Zank,et al.  A revealed reference point for prospect theory , 2019 .

[29]  Fuyuan Xiao,et al.  A Multiple-Criteria Decision-Making Method Based on D Numbers and Belief Entropy , 2019, International Journal of Fuzzy Systems.

[30]  Xinyang Deng,et al.  D number theory based game-theoretic framework in adversarial decision making under a fuzzy environment , 2019, Int. J. Approx. Reason..

[31]  Jabez Christopher,et al.  The Science of Rule-based Classifiers , 2019, 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence).

[32]  Yong Deng,et al.  Combining conflicting evidence using the DEMATEL method , 2018, Soft Comput..

[33]  Ying-Ming Wang,et al.  An Emergency Decision Making Method Based on Prospect Theory for Different Emergency Situations , 2018, International Journal of Disaster Risk Science.

[34]  Nikhil R. Pal,et al.  Weighted Fuzzy Dempster–Shafer Framework for Multimodal Information Integration , 2018, IEEE Transactions on Fuzzy Systems.

[35]  Jindong Qin,et al.  Three-way group decisions based on prospect theory , 2018, J. Oper. Res. Soc..

[36]  Yong Deng,et al.  A method to determine basic probability assignment in the open world and its application in data fusion and classification , 2017, Applied Intelligence.

[37]  Yong Deng,et al.  A method to determine basic probability assignment in the open world and its application in data fusion and classification , 2016, Applied Intelligence.

[38]  Zeshui Xu,et al.  Probabilistic linguistic term sets in multi-attribute group decision making , 2016, Inf. Sci..

[39]  Hongbin Zha,et al.  Evidential calibration of binary SVM classifiers , 2016, Int. J. Approx. Reason..

[40]  Ying-Ming Wang,et al.  A prospect theory-based interval dynamic reference point method for emergency decision making , 2015, Expert Syst. Appl..

[41]  S. Mahadevan,et al.  A non-parametric method to determine basic probability assignment for classification problems , 2014, Applied Intelligence.

[42]  Sankaran Mahadevan,et al.  A non-parametric method to determine basic probability assignment for classification problems , 2014, Applied Intelligence.

[43]  Xiao-hong Chen,et al.  Extension of the TOPSIS method based on prospect theory and trapezoidal intuitionistic fuzzy numbers for group decision making , 2014 .

[44]  Jianwen Chen,et al.  Dealing with Uncertainty: A Survey of Theories and Practices , 2013, IEEE Transactions on Knowledge and Data Engineering.

[45]  Sankaran Mahadevan,et al.  A new method to determine basic probability assignment from training data , 2013, Knowledge-Based Systems.

[46]  Mandava Rajeswari,et al.  A survey of the state of the art in learning the kernels , 2012, Knowledge and Information Systems.

[47]  Xin Zhang,et al.  Research on the multi-attribute decision-making under risk with interval probability based on prospect theory and the uncertain linguistic variables , 2011, Knowl. Based Syst..

[48]  Yiyu Yao,et al.  The superiority of three-way decisions in probabilistic rough set models , 2011, Inf. Sci..

[49]  Yiyu Yao,et al.  Three-way Investment Decisions with Decision-theoretic Rough Sets , 2011, Int. J. Comput. Intell. Syst..

[50]  Yiyu Yao,et al.  Three-way decisions with probabilistic rough sets , 2010, Inf. Sci..

[51]  Tony R. Martinez,et al.  Distribution-balanced stratified cross-validation for accuracy estimation , 2000, J. Exp. Theor. Artif. Intell..

[52]  Thierry Denoeux,et al.  A k-nearest neighbor classification rule based on Dempster-Shafer theory , 1995, IEEE Trans. Syst. Man Cybern..

[53]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

[54]  B. Dahn.  Foundations of Probability theory, statistical inference, and statistical theories of science , 1978 .

[55]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[56]  Chunmao Jiang,et al.  A novel outcome evaluation model of three-way decision: A change viewpoint , 2022, Inf. Sci..

[57]  Aristoteles Aristoteles,et al.  Comparative Analysis of Cow Disease Diagnosis Expert System using Bayesian Network and Dempster-Shafer Method , 2019, International Journal of Advanced Computer Science and Applications.

[58]  Fuyuan Xiao,et al.  A Non-Parametric Method to Determine Basic Probability Assignment Based on Kernel Density Estimation , 2018, IEEE Access.

[59]  Uwe Fink,et al.  Classic Works Of The Dempster Shafer Theory Of Belief Functions , 2016 .

[60]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[61]  E. Jaynes,et al.  Confidence Intervals vs Bayesian Intervals , 1976 .