Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making

Abstract Intuitionistic fuzzy sets are useful for modeling uncertain data of realistic problems. In this paper, we generalize and expand the utility of complex intuitionistic fuzzy sets using the space of quaternion numbers. The proposed representation can capture composite features and convey multi-dimensional fuzzy information via the functions of real membership, imaginary membership, real non-membership, and imaginary non-membership. We analyze the order relations and logic operations of the complex intuitionistic fuzzy set theory and introduce new operations based on quaternion numbers. We also present two quaternion distance measures in algebraic and polar forms and analyze their properties. We apply the quaternion representations and measures to decision-making models. The proposed model is experimentally validated in medical diagnosis, which is an emerging application for tackling patient’s symptoms and attributes of diseases.

[1]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[2]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[3]  Pham Hong Phong,et al.  Linguistic approach in medical diagnosis , 2016, 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE).

[4]  Junjun Mao,et al.  A novel cross-entropy and entropy measures of IFSs and their applications , 2013, Knowl. Based Syst..

[5]  Bijan Davvaz,et al.  An application of intuitionistic fuzzy sets in medicine , 2016 .

[6]  Yejun Xu,et al.  Methods to improve the ordinal and multiplicative consistency for reciprocal preference relations , 2018, Appl. Soft Comput..

[7]  William Rowan Hamilton,et al.  ON QUATERNIONS, OR ON A NEW SYSTEM OF IMAGINARIES IN ALGEBRA , 1847 .

[8]  Gang Kou,et al.  A review on trust propagation and opinion dynamics in social networks and group decision making frameworks , 2019, Inf. Sci..

[9]  Payman Moallem,et al.  Training Echo State Neural Network Using Harmony Search Algorithm , 2017 .

[10]  Etienne E. Kerre,et al.  On the position of intuitionistic fuzzy set theory in the framework of theories modelling imprecision , 2007, Inf. Sci..

[11]  Dan E. Tamir,et al.  Complex Number Representation of Intuitionistic Fuzzy Sets , 2016 .

[12]  Abdul Razak Salleh,et al.  Complex intuitionistic fuzzy sets , 2012 .

[13]  Krassimir T. Atanassov,et al.  On Intuitionistic Fuzzy Sets Theory , 2012, Studies in Fuzziness and Soft Computing.

[14]  Janusz Kacprzyk,et al.  A Similarity Measure for Intuitionistic Fuzzy Sets and Its Application in Supporting Medical Diagnostic Reasoning , 2004, ICAISC.

[15]  Abraham Kandel,et al.  A new interpretation of complex membership grade , 2011, Int. J. Intell. Syst..

[16]  Amit Srivastava,et al.  Study on divergence measures for intuitionistic fuzzy sets and its application in medical diagnosis , 2016 .

[17]  Nguyen Thanh Tung,et al.  Segmentation of dental X-ray images in medical imaging using neutrosophic orthogonal matrices , 2018, Expert Syst. Appl..

[18]  Oleksii K. Tyshchenko,et al.  A neuro-fuzzy Kohonen network for data stream possibilistic clustering and its online self-learning procedure , 2017, Appl. Soft Comput..

[19]  Václav Snásel,et al.  Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree , 2016, Journal of Medical Systems.

[20]  Saifur Rahman,et al.  On cuts of Atanassov's intuitionistic fuzzy sets with respect to fuzzy connectives , 2016, Inf. Sci..

[21]  Abraham Kandel,et al.  Complex intuitionistic fuzzy classes , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[22]  Fatemeh Afsari,et al.  Hesitant fuzzy decision tree approach for highly imbalanced data classification , 2017, Appl. Soft Comput..

[23]  Abraham Kandel,et al.  Axiomatic Theory of Complex Fuzzy Logic and Complex Fuzzy Classes , 2011, Int. J. Comput. Commun. Control.

[24]  Abd Ulazeez M. Alkouri,et al.  Complex Atanassov's Intuitionistic Fuzzy Relation , 2013 .

[25]  Shaocheng Tong,et al.  Adaptive fuzzy control of uncertain stochastic nonlinear systems with unknown dead zone using small-gain approach , 2014, Fuzzy Sets Syst..

[26]  Ioannis K. Vlachos,et al.  Intuitionistic fuzzy information - Applications to pattern recognition , 2007, Pattern Recognit. Lett..

[27]  Janusz Kacprzyk,et al.  Distances between intuitionistic fuzzy sets , 2000, Fuzzy Sets Syst..

[28]  Abraham Kandel,et al.  On complex fuzzy sets , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[29]  Abraham Kandel,et al.  Complex fuzzy logic , 2003, IEEE Trans. Fuzzy Syst..

[30]  Witold Pedrycz,et al.  Building consensus in group decision making with an allocation of information granularity , 2014, Fuzzy Sets Syst..

[31]  R. Parvathi,et al.  Intuitionistic fuzzification functions , 2016 .

[32]  Kuo-Chen Hung,et al.  Medical Pattern Recognition: Applying an Improved Intuitionistic Fuzzy Cross-Entropy Approach , 2012, Adv. Fuzzy Syst..

[33]  Krassimir T. Atanassov,et al.  Intuitionistic Fuzzy Sets - Theory and Applications , 1999, Studies in Fuzziness and Soft Computing.

[34]  Enrique Herrera-Viedma,et al.  A comparative study on consensus measures in group decision making , 2018, Int. J. Intell. Syst..

[35]  Weiqiong Wang,et al.  Distance measure between intuitionistic fuzzy sets , 2005, Pattern Recognit. Lett..

[36]  Enrique Herrera-Viedma,et al.  A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling , 2010 .

[37]  Yejun Xu,et al.  Consensus model for large-scale group decision making based on fuzzy preference relation with self-confidence: Detecting and managing overconfidence behaviors , 2019, Inf. Fusion.

[38]  Jie Lu,et al.  A Method for Multiple Periodic Factor Prediction Problems Using Complex Fuzzy Sets , 2012, IEEE Transactions on Fuzzy Systems.

[39]  Witold Pedrycz,et al.  Efficient mining product-based fuzzy association rules through central limit theorem , 2018, Appl. Soft Comput..

[40]  Enrique Herrera-Viedma,et al.  A Self-Management Mechanism for Noncooperative Behaviors in Large-Scale Group Consensus Reaching Processes , 2018, IEEE Transactions on Fuzzy Systems.

[41]  Mumtaz Ali,et al.  H-max distance measure of intuitionistic fuzzy sets in decision making , 2018, Appl. Soft Comput..

[42]  Jun Ye,et al.  Improved intuitionistic fuzzy cross-entropy and its application to pattern recognitions , 2010, 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering.

[43]  Sheng-Yi Jiang,et al.  A note on information entropy measures for vague sets and its applications , 2008, Inf. Sci..

[44]  Chris Cornelis,et al.  Intuitionistic Fuzzy Relational Images , 2005, Computational Intelligence for Modelling and Prediction.

[45]  Igor Skrjanc,et al.  New results in modelling derived from Bayesian filtering , 2010, Knowl. Based Syst..

[46]  William Rowan Hamilton,et al.  XI. On quaternions; or on a new system of imaginaries in algebra , 1848 .

[47]  Benjamín R. C. Bedregal,et al.  Fuzzy quaternion numbers , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[48]  Abd Ulazeez M. Alkouri,et al.  Some operations on complex Atanassov's intuitionistic fuzzy sets , 2013 .