Abstract

The concept of fuzzy classification has been significantly used in various Purposes. The fuzzy classification area has been increased rapidly in the past few years and it has been successfully adopted . In this work , we propose to develop a means to understand Fuzzy Classification . Particularly , this article tends to present a deep review of the most important topics of Fuzzy classification including new improvements in the field. This article explains the significance of Fuzzy classification, displays the various methods of Fuzzy classification and different applications . The paper ends with a summary and conclusion.

References

• L.A. Zadeh, Fuzzy Sets, Information and Control 8:338-353 (1965)
• L.A. Zadeh, Outline of a new apporach to the analysis of complex systems and decision processes, IEEE transactions on Systems, Man and cybernetics 1:- (1973)
• J.C. Bezdek, Fuzzy models for pattern recognition: background, significance and key points. In: J.C. Bezdek and S.K. Pal, eds.; fizzy Models for Pattern Recognition (IEEE Press, New York, 1992); pages 1-27
• Arif M, Akram MU, Minhas FA (2010) Pruned fuzzy k-nearest neighbor classifier for beat classification. J Biomed Sci Eng 3:380–3899
• Chen SM, Chang YC (2010) Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques. Inf Sci 180:4772–4783
• Chen S, Chen L (2007) A fuzzy hierarchical clustering method for clustering documents based on dynamic cluster centers. J Chin Inst Eng 30:169–172
• Chen SM, Ke JS, Chang JF (1990) Knowledge representation using fuzzy petri nets. IEEE Trans Knowl Data Eng 2:311–319
• J. M. Keller, M. R. Gray and J. A. Givens, "A fuzzy K-nearest neighbor algorithm," in IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-15, no. 4, pp. 580-585, July-Aug. 1985, doi: 10.1109/TSMC.1985.6313426.
• Chen HL, Huang CC, Yu XG, Xu X, Sun X, Wang G, Wang SJ (2013) An efficient diagnosis system for detection of parkinson’s disease using fuzzy k-nearest neighbor approach. Expert Syst Appl 40(1):263–271
• Hamzah MI, Abdalla TY, “ Mobile robot navigation using fuzzy logic and wavelet network”  - IAES International Journal of Robotics and Automation, vol.3 , No.3,2014
• Abdul Zahra AK, Abdalla TY, “ Adaptive Fuzzy Super–Twisting Sliding Mode Controller optimized by ABC for Vehicle”, Basrah Journal for engineering science 19 (2), 9-17,2019
• Abdalla TY,”Adaptive Fuzzy FOPID Control Scheme for Path tracking of Mobile Robot”, International Journal of Computer Applications. Vol.181,12 ,2018
• Abdul Zahra AK, Abdalla TY “An ABC Optimized Adaptive Fuzzy Sliding Mode Control Strategy for Full Vehicle Active suspension system”,Iraqi Journal for Electrical & Electronic Engineering 17 (2), 2021
• Al-Mutar WH, Abdalla TY , “Quarter car active suspension system control using fuzzy controller tuned by PSO” , International journal of computer applications, 2015
• Ahmed AA, Abdalla TY, Abed AA,  “Path Planning of Mobile Robot Using Fuzzy-Potential Field Method”, Iraqi Journal for Electrical & Electronic Engineering, vol.11, No.1 .2015
• Abdalla TY, Abdulkareem A, “ A PSO optimized fuzzy control scheme for mobile robot path tracking “, International Journal of Computer Applications, vol.76,NO.2,2013
• Abdul Zahra AK, Abdalla TY, “ Design of fuzzy super twisting sliding mode control scheme for unknown full vehicle active suspension systems using an artificial bee colony optimization algorithm”,   Asian Journal of Control, vol.23,No.4,2021
• Abdalla TY, "Fuzzy Fine tuning of an Optimized PID Control Scheme for Mobile Robot Trajectory Tracking”,  Int J Comput Appl vol. 181, 2018
• Nasar KA, Abdalla TY, Abdalla AY ,” Computer Network Routing Using Fuzzy Neural Networks “, Basrah Journal of Science, vol.31, No.2, 2013
• Qilian Liang and Jerry M. “Mendel MPEG VBR Video Traffic Modeling and Classification Using Fuzzy Technique”, IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 9, NO. 1, FEBRUARY 2001
• Uraiwan I. , Phayung M., Choochart H “Terrorism Event Classification using Fuzzy Inference Systems” (IJCSIS) International Journal of Computer Science and Information Security, Vol. 7, No. 3, 2010
• Muntaser A., Nazar E. “West of Iraq satellite image classification using fuzzy logic”, Journal of Kufa for Mathematics and Computer Vol.1, No.4, Nov., 2011, pp.36- 48 .
• Min Tang1, Xia Chen1, Weidong Hu1, and Wenxian Yu, “A Fuzzy Rule-Based Classification System Using Interval Type-2 Fuzzy Sets”, International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making IUKM 2011
• H Chen, HL., Liu, DY., Yang, B., Liu, J., Wang, G., Wang, SJ. (2011). An Adaptive Fuzzy k-Nearest Neighbor Method Based on Parallel Particle Swarm Optimization for Bankruptcy Prediction. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science. Vol. 6634, (PART 1) , pp 249–264. Springer, Berlin, Heidelberg. Doi:10.1007/978-3-642-20841-6_21
• Payman M, and B. Somayeh Mousav,” Gender Classification by Fuzzy Inference System” , International Journal of Advanced Robotic Systems , 2013, Vol. 10, 89:2013
• Reza Ali M., Seyed M. A., Somayeh B. and Ali G., " Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease”, Computational and Mathematical Methods in Medicine Volume 2015, Article ID 564867, 8 pages http://dx.doi.org/10.1155/2015/564867
• Kanika B. and Yogita G., “Classification using Fuzzy Cognitive Maps & Fuzzy Inference System”, Journal of Basic and Applied Engineering Research Print ISSN: 2350-0077; Online ISSN: 2350-0255; Volume 2, Number 2; January-March, 2015, pp. 159-163
• Chetna N. , Upadhyay PK.,” Sleep EEG Classification Using Fuzzy Logic”, International Journal of Recent Development in Engineering and Technology Volume 4, Special Issue 1, May 2015
• Taneja S., Suri B., Narwal H., Jain A., Kathuria A. and Gupta S., "A new approach for data classification using Fuzzy logic," 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), 2016, pp. 22-27, doi: 10.1109/CONFLUENCE.2016.7508041.
• Łapa, K., Cpałka, K. (2016). Nonlinear Pattern Classification Using Fuzzy System and Hybrid Genetic-Imperialist Algorithm. In: Wilimowska, Z., Borzemski, L., Grzech, A., Świątek, J. (eds) Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology – ISAT 2015 – Part IV. Advances in Intelligent Systems and Computing, vol 432. Springer, doi:10.1007/978-3-319-28567-2_14
• Shuma A. , Nidul S. and Thingam D., “Fuzzy logic based online fault detection and classification in transmission line”, Springer (2016) 5:1002, DOI 10.1186/s40064-016-2669-4
• Ravi K. R. and Jayanthi J., “Student prediction system for placement training using fuzzy inference system“, ICTACT journal on soft computing, 2017, Volume: 07, Issue: 03, doi: 10.21917/ijsc.2017.0199
• Mohammad M., Rahib A., and Idoko J., “Intelligent Classification of Liver Disorder using Fuzzy Neural System”, International Journal of Adv. Computer Science and Applications, Vol. 8, No. 12, 2017.
• Han Liu, Pete Burnap, Wafa A. and Matthew L. W., “A Fuzzy Approach to Text Classification with Two Stage Training for Ambiguous Instances”. IEEE Transections On computational social systems , vol. 5 , 2019
• Sree K, , Hima S., Jayadeep K., Lakshmi P, “Text Classification Using Fuzzy Neural Network” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7, Issue-6S4, April 2019
• Martin T. , Adrian C. , Adam C . and Adam D. ” Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference “Appl. Sci. 2021, 11, 3484. https://doi.org/10.3390/app 11083484
• Vipul M. “Use Neuro-Fuzzy System for Classification” International Journal of Engineering Research & Technology (IJERT), Vol. 10 Issue 08, August-2021
• Idris NF, Ismail MA, “ Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition”, PeerJ Comput. Sci. 7:e427 DOI 10.7717/peerj-cs.427
• Aamir, K.M.; Sarfraz, L.; Ramzan, M.; Bilal, M.; Shafi, J.; Attique, M. A Fuzzy Rule-Based System for Classification of Diabetes. Sensors 2021, 21, 8095. https:// doi.org/10.3390/s21238095
• Mahinda M., Kumbure1 L. “A generalized fuzzy k-nearest neighbor regression model based on Minkowski distance “ , Granular Computing (2022) 7:657–671 https://doi.org/10.1007/s41066-021-00288-w.