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A Survey of Fuzzy Pattern Tree Classification Algorithms

Ji Zhang, Bin Ma
Vol. 5, No. 4, pp. 49-55, 2023 DOI: 10.54691/sjt.v5i4.4738 April 20, 2023
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Abstract.Data classification algorithm is the core content of big data mining. Its main function is to extract valuable knowledge and information,analyze the characteristics of all kinds of information, and provide data basis for further research. With the wide application of data mining technology, data classification algorithms continue to emerge and gradually improve. The classic classification algorithms include decision tree classification algorithm, naive Bayes algorithm, support vector machine classification algorithm, artificial neural network classification algorithm, fuzzy pattern tree (FPT) and so on. This paper summarizes several common algorithms in data classification algorithms, and analyzes their characteristics to understand their algorithm principles and application scenarios.
Keywords. FPT; Classification Algorithm; Data Classification.
APA Citation Ji Zhang, Bin Ma (2023). A Survey of Fuzzy Pattern Tree Classification Algorithms. Scientific Journal of Technology, 5(4), 49-55. https://doi.org/10.54691/sjt.v5i4.4738

References

  1. Yin Tingjun, Li Linghui, Zhou Rui. Overview of data classification algorithms in big data mining [J]. Digital Technology and Application, 2021,39 (01): 102-104.
  2. Yu Jianjun, Zhang Qiongzhi. Rough Set Based Decision Tree ID3 Algorithm [J]. Computer System Application, 2020,29 (04): 156-162.
  3. Guo Xingchen, Wang Qingqing, Wang Ya. Research on the application of decision tree algorithm in medical data classification [J]. Journal of Anqing Normal University (Natural Science Edition), 2021,27 (02): 49-53.
  4. Chen Leishi, Gao Xiaxia, Liao Yufang, Deng Jianbo, Zhou Bi. A Classification Method for Wetlands in Dongting Lake Area Based on the CART Decision Tree [J]. Surveying and Mapping Bulletin, 2021, {4} (06): 12-15.
  5. Li Yingchun Research on Decision Tree Classification Algorithms in Data Mining [D]. Hunan Normal University, 2015.
  6. Lin Yifeng Application of Decision Tree Algorithm in Student Achievement Prediction [D]. Ningbo University, 2019.
  7. Cheng Yifang. Overview of Data Classification Algorithms in Data Mining [J]. Digital Communications World, 2021, {4} (02): 136-137+140.
  8. Chen Hong et al. Improved naive Bayes classification algorithm for traffic risk management[J]. EURASIP Journal on Advances in Signal Processing, 2021, 2021(1).
  9. Salau Jennifer et al. Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification. [J]. Animals: an open access journal from MDPI, 2020, 11(1).
  10. Wang Shaowei, Xu Feng, Chao Haitao, Liu Yu, Zuo Dunwen. Research on fault classification of textile hot rolling mills based on support vector machines [J]. Modern Manufacturing Engineering, 2021, {4} (06): 116-121.
  11. Yang Fan, He Zhengwei, He Fan. Research on channel water depth prediction method based on depth network and ship traffic flow [J]. Journal of Wuhan University of Technology (Traffic Science and Engineering Edition), 2019,43 (01): 130-135.
  12. L. X. Wang and J. M. Mendel, “Generating fuzzy rules by learning from examples,” IEEE Trans. Syst., Man, Cybern., vol. 22, no. 6, pp. 1414–1427, Nov./Dec. 1992.
  13. J. R. Quinlan, “Decision trees and decision making,” IEEE Trans. Syst.,Man, Cybern., vol. 20, no. 2, pp. 339–346, Mar./Apr. 1990.
  14. Y. Yuan and M. J. Shaw, “Induction of fuzzy decision trees,” Fuzzy Sets Syst., vol. 69, no. 2, pp. 125–139, 1995.
  15. C. Z. Janikow, “Fuzzy decision trees: Issues and methods,” IEEE Trans. Syst., Man, Cybern., vol. 28, no. 1, pp. 1–14, Feb. 1998.
  16. C. Olaru and L. Wehenkel, “A complete fuzzy decision tree technique,”Fuzzy Sets Syst., vol. 138, no. 2, pp. 221–254, 2003.
  17. A. Suarez and J. F. Lutsko, “Globally optimal fuzzy decision trees for ´classification and regression,” IEEE Trans. Pattern Anal. Mach. Intell.,vol. 21, no. 12, pp. 1297–1311, Dec. 1999.
  18. X. Wang, B. Chen, G. Olan, and F. Ye, “On the optimization of fuzzy decision trees,” Fuzzy Sets Syst., vol. 112, no. 1, pp. 117–125, 2000.
  19. S. M. Chen, S. H. Lee, and C. H. Lee, “A new method for generating fuzzy rules from numerical data for handling classification problems,” Appl. Artif. Intell., vol. 15, no. 7, pp. 645–664, 2001.
  20. K. A. Rasmani and Q. Shen, “Weighted linguistic modelling based on fuzzy subsethood values,” in Proc. IEEE Int. Conf. Fuzzy Syst., 2003, vol. 1, pp. 714–719.
  21. L. A. Zadeh, “Fuzzy sets,” Inf. Control, vol. 8, no. 3, pp. 338–353, 1965.
  22. Z. Huang, T. D. Gedeon and M. Nikravesh, "Pattern Trees Induction: A New Machine Learning Method," in IEEE Transactions on Fuzzy Systems, vol. 16, no. 4, pp. 958-970, Aug. 2008, doi: 10.1109/TFUZZ.2008.924348.
  23. R. Senge and E. Hüllermeier, "Top-Down Induction of Fuzzy Pattern Trees," in IEEE Transactions on Fuzzy Systems, vol. 19, no. 2, pp. 241-252, April 2011, doi: 10.1109/TFUZZ.2010.2093532.
  24. Ammar Shaker and Robin Senge and Eyke Hüllermeier. Evolving fuzzy pattern trees for binary classification on data streams[J]. Information Sciences, 2013, 220 : 34-45.
  25. P. Su, T. Chen, H. Mao, J. Xie, Y. Zhao and J. Liu, "On the Application of Preaggregation Functions to Fuzzy Pattern Tree," 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019, pp. 1-6, doi: 10.1109/FUZZ-IEEE.2019.8858922.
  26. A. Rodrigues dos Santos, J. L. Machado do Amaral, C. A. Ribeiro Soares and A. Valladão de Barros, "Multi-objective Fuzzy Pattern Trees," 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018, pp. 1-6, doi: 10.1109/FUZZ-IEEE.2018.8491689.
  27. Xinmin Zhang and Manabu Kano and Shinroku Matsuzaki. Ensemble pattern trees for predicting hot metal temperature in blast furnace[J]. Computers and Chemical Engineering, 2019, 121 : 442-449.
  28. Robin Senge et al. Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy-Modellierung[J]. Methoden und Anwendungen der Steuerungs-, Regelungs- und Informationstechnik, 2012, 60(10) : 622-629.
  29. Dovom E M , Azmoodeh A , Dehghantanha A , et al. Fuzzy Pattern Tree for Edge Malware Detection and Categorization in IoT[J]. Journal of Systems Architecture, 2019, 97:1-7.
  30. R. Senge and E. Hüllermeier, "Fast Fuzzy Pattern Tree Learning for Classification," in IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 2024-2033, Dec. 2015, doi: 10.1109/ TFUZZ. 2015. 2396078.
  31. M. Islam, D. T. Anderson, A. J. Pinar, T. C. Havens, G. Scott and J. M. Keller, "Enabling Explainable Fusion in Deep Learning With Fuzzy Integral Neural Networks," in IEEE Transactions on Fuzzy Systems, vol. 28, no. 7, pp. 1291-1300, July 2020, doi: 10.1109/TFUZZ.2019.2917124.