Multiple multidimensional linguistic reasoning algorithm based on property-oriented linguistic concept lattice

Abstract Aiming at the difficult problems of dealing with mass linguistic information in uncertain environment, this paper mainly focuses on a linguistic reasoning algorithm based on property-oriented linguistic concept lattice by combining concept lattice and neural network. Specifically, we present a property-oriented linguistic concept lattice to express linguistic information between concepts based on linguistic term set. Furthermore, we are devoted to the study of rule extraction of fuzzy linguistic formal decision context by constructing property-oriented linguistic decision concepts. In order to obtain more decision knowledge from known linguistic rules, an extension method of linguistic decision rules is developed, which takes advantage of the dominance relation between similar rules on the premise of consistent property-oriented linguistic decision rules. In addition, we construct a multiple multidimensional linguistic reasoning model for predicting uncertain decision information. Moreover, we input a multiple multidimensional linguistic reasoning model with property-oriented linguistic decision information into neural network to obtain unknown decision results, which can not only improve the accuracy of inference results but also reduce the loss of linguistic information. Finally, some experiments are conducted to demonstrate the efficiency of the proposed method.

[1]  Huifang Deng,et al.  Using Fuzzy Concept Lattice for Intelligent Disease Diagnosis , 2017, IEEE Access.

[2]  A. Tye,et al.  Bayesian population correlation: A probabilistic approach to inferring and comparing population distributions for detrital zircon ages , 2019, Chemical Geology.

[3]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[4]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..

[5]  Ming-Wen Shao,et al.  The construction of attribute (object)-oriented multi-granularity concept lattices , 2020, Int. J. Mach. Learn. Cybern..

[6]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[7]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[8]  Francisco Herrera,et al.  Hesitant Fuzzy Linguistic Term Sets for Decision Making , 2012, IEEE Transactions on Fuzzy Systems.

[9]  Zaili Yang,et al.  Use of Fuzzy Risk Assessment in FMEA of Offshore Engineering Systems , 2015 .

[10]  Bo Li,et al.  Attribute reduction and rule acquisition of formal decision context based on object (property) oriented concept lattices , 2019, Int. J. Mach. Learn. Cybern..

[11]  Junzo Watada,et al.  Gaussian-PSO with fuzzy reasoning based on structural learning for training a Neural Network , 2016, Neurocomputing.

[12]  Yee Leung,et al.  Knowledge acquisition in incomplete information systems: A rough set approach , 2006, Eur. J. Oper. Res..

[13]  Keyun Qin,et al.  Three-way decision with incomplete information based on similarity and satisfiability , 2020, Int. J. Approx. Reason..

[14]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[15]  C. Guedes Soares,et al.  Incorporating evidential reasoning and TOPSIS into group decision-making under uncertainty for handling ship without command , 2018, Ocean Engineering.

[16]  Prem Kumar Singh,et al.  Object and attribute oriented m-polar fuzzy concept lattice using the projection operator , 2018, Granular Computing.

[17]  E. H. Mamdani,et al.  Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis , 1976, IEEE Transactions on Computers.

[18]  Huilai Zhi,et al.  Three-way dual concept analysis , 2019, Int. J. Approx. Reason..

[19]  Branko Ristic,et al.  A tutorial on uncertainty modeling for machine reasoning , 2020, Inf. Fusion.

[20]  Rafael Peñaloza,et al.  Algorithms for reasoning in very expressive description logics under infinitely valued Gödel semantics , 2017, Int. J. Approx. Reason..

[21]  Jian-Min Ma,et al.  Concept acquisition approach of object-oriented concept lattices , 2017, Int. J. Mach. Learn. Cybern..

[22]  Xin Wen,et al.  Linguistic truth-valued intuitionistic fuzzy reasoning with applications in human factors engineering , 2016, Inf. Sci..

[23]  Xizhao Wang,et al.  Comparison of reduction in formal decision contexts , 2017, Int. J. Approx. Reason..

[24]  Zhiwei Zhao,et al.  Multiple multidimensional fuzzy reasoning algorithm based on neural network , 2018, J. Intell. Fuzzy Syst..

[25]  Juan Carlos Augusto,et al.  A group decision making model for partially ordered preference under uncertainty , 2015, Inf. Fusion.

[26]  Jinhai Li,et al.  Granule description based knowledge discovery from incomplete formal contexts via necessary attribute analysis , 2019, Inf. Sci..

[27]  Jinhai Li,et al.  Incomplete decision contexts: Approximate concept construction, rule acquisition and knowledge reduction , 2013, Int. J. Approx. Reason..

[28]  Huawen Liu,et al.  Unified forms of fully implicational restriction methods for fuzzy reasoning , 2007, Inf. Sci..

[29]  Prem Kumar Singh,et al.  M-polar Fuzzy Graph Representation of Concept Lattice , 2018, Eng. Appl. Artif. Intell..

[30]  Ming-Wen Shao,et al.  Attribute reduction in generalized one-sided formal contexts , 2017, Inf. Sci..

[31]  Xiao Zhang,et al.  Rule-preserved object compression in formal decision contexts using concept lattices , 2014, Knowl. Based Syst..

[32]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[33]  Yiyu Yao,et al.  Concept lattices in rough set theory , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[34]  Yiming Cao,et al.  A method of multimedia teaching evaluation based on fuzzy linguistic concept lattice , 2019, Multimedia Tools and Applications.

[35]  Zhi-Hua Zhou,et al.  Abductive learning: towards bridging machine learning and logical reasoning , 2019, Science China Information Sciences.

[36]  Tao Zhang,et al.  Incremental concept-cognitive learning based on attribute topology , 2020, Int. J. Approx. Reason..

[37]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[38]  Francisco Herrera,et al.  Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions , 2018, Knowl. Based Syst..

[39]  Witold Pedrycz,et al.  Long-term forecasting of time series based on linear fuzzy information granules and fuzzy inference system , 2017, Int. J. Approx. Reason..