| Title:
|
Efficiency analysis of the rule-based defuzzification approach to fuzzy inference system for regression problems (English) |
| Author:
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Nasiboglu, Resmiye |
| Author:
|
Nasibov, Efendi |
| Language:
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English |
| Journal:
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Kybernetika |
| ISSN:
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0023-5954 (print) |
| ISSN:
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1805-949X (online) |
| Volume:
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61 |
| Issue:
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1 |
| Year:
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2025 |
| Pages:
|
109-132 |
| Summary lang:
|
English |
| . |
| Category:
|
math |
| . |
| Summary:
|
A fuzzy inference system (FIS) is an effective prediction method based on fuzzy logic. The performance of this model may vary depending on the defuzzification process. In the Mamdani-type FIS model, the defuzzification process is applied to the fuzzy output of the system only once at the last stage. In the FIS with rule-based defuzzification (FIS-RBD) model, the defuzzification process is applied to the fuzzy consequent part of each rule and the overall result of the system is calculated as the weighted average of the separately defuzzified results of the rules. Note that, the original shapes of the combined rule results are lost in the aggregated fuzzy result of the classical Mamdani-type system and the effect of each rule on the system result decreases when aggregated. However, rule results can affect the overall result more significantly in the FIS-RBD approach. In this study, a comparative analysis was made on the effectiveness of the classical Mamdani-type FIS and FIS-RBD models for regression problems. Five datasets from different domains and various defuzzification methods were used in comparisons. In the results obtained, it was observed that the The FIS-RBD model gave better results than the classical Mamdani-type FIS model. To carry out calculation experiments, a new Python package called Fuzlab was developed by modifying the existing Python library called FuzzyLab. In addition to creating the FIS-RBD model, the developed package also allows the use of the Weighted Average Based on Levels (WABL) defuzzification method in fuzzy logic-based calculations. (English) |
| Keyword:
|
fuzzy inference system (FIS) |
| Keyword:
|
defuzzification |
| Keyword:
|
rule-based defuzzification (RBD) |
| Keyword:
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regression |
| Keyword:
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Python library |
| MSC:
|
68N30 |
| MSC:
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68T05 |
| MSC:
|
93C42 |
| DOI:
|
10.14736/kyb-2025-1-0109 |
| . |
| Date available:
|
2025-04-07T09:44:17Z |
| Last updated:
|
2025-04-07 |
| Stable URL:
|
http://hdl.handle.net/10338.dmlcz/152927 |
| . |
| Reference:
|
[1] Aggarwal, A., Chakradar, M., Bhatia, M. S., Kumar, M., Stephan, T., Gupta, S. K., Alsamhi, H. S., AL-Dois, H.: COVID-19 Risk prediction for diabetic patients using fuzzy inference system and machine learning approaches..J. Healthcare Engrg. (2022), Article ID 4096950. |
| Reference:
|
[2] Amrahov, S. E., Ar, Y., Tugrul, B., Akay, B. E., Kartli, N.: A new approach to Mergesort algorithm: Divide smart and conquer..Future Generation Computer Systems 157 (2024), 330-343. |
| Reference:
|
[3] Ansarifar, J., Wang, L., Archontoulis, S. V.: An interaction regression model for crop yield prediction..Scientific Reports 11 (2021), Article ID 17754. |
| Reference:
|
[4] Ao, Y., Li, H., Zhu, L., Ali, S., Yang, Z.: The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling..J. Petroleum Sci. Engrg. 174 (2019), 776-789. |
| Reference:
|
[5] Ar, Y., Amrahov, S. E., Gasilov, N. A., Yigit-Sert, S.: A new curve fitting based rating prediction algorithm for recommender systems..Kybernetika 58 (2022), 3, 440-455. |
| Reference:
|
[6] Avelar, E., Castillo, O., Soria, J.: Fuzzy logic controller with fuzzylab Python library and the robot operating system for autonomous mobile robot navigation..J. Automat. Mobile Robotics Intell. Systems 14 (2019), 1, 48-54. |
| Reference:
|
[7] Avelar, E.: FuzzyLab.. |
| Reference:
|
[8] Bas, E., Egrioglu, E.: A fuzzy regression functions approach based on Gustafson-Kessel clustering algorithm..Inform. Sci. 592 (2022), 206-214. |
| Reference:
|
[9] Bas, E.: Robust fuzzy regression functions approaches..Inform. Sci. 613 (2022), 419-434. |
| Reference:
|
[10] Bejines, C.: Aggregation of fuzzy vector spaces..Kybernetika 59 (2023), 5, 752-767. MR 4681021, |
| Reference:
|
[11] Chakraverty, S., Sahoo, D. M., Mahato, N. R.: Defuzzification..In: Concepts of Soft Computing, Springer, Singapore 2019. |
| Reference:
|
[12] Charizanos, G., Demirhan, H., İçen, D.: A Monte Carlo fuzzy logistic regression framework against imbalance and separation..Inform. Sci. 655 (2024), 119893. |
| Reference:
|
[13] Cruz-Suárez, H., Montes-de-Oca, R., Ortega-Gutiérrez, R. I.: An extended version of average Markov decision processes on discrete spaces under fuzzy environment..Kybernetika 59 (2023), 1, 160-178. MR 4567846, |
| Reference:
|
[14] Ding, W., Wang, J., Huang, J., Cheng, C., Jiang, S.: MFCA: Collaborative prediction algorithm of brain age based on multimodal fuzzy feature fusion..Inform. Sci. 687 (2025), 121376. |
| Reference:
|
[15] Doz, D., Cotič, M., Felda, D.: Random forest regression in predicting students' achievements and fuzzy grades..Mathematics 11 (2023), 19, 4129. |
| Reference:
|
[16] Fiskin, R., Atik, O., Kisi, H., Nasibov, E., Johansen, T. A.: Fuzzy domain and meta-heuristic algorithm-based collision avoidance control for ships: Experimental validation in virtual and real environment..Ocean Engrg. 220 (2021), 108502. |
| Reference:
|
[17] Gao, K., Xu, L.: Novel strategies based on a gradient boosting regression tree predictor for dynamic multi-objective optimization..Expert Syst. Appl. 237 (2024), 121532. |
| Reference:
|
[18] Gao, T., Liu, J.: Application of improved random forest algorithm and fuzzy mathematics in physical fitness of athletes..J. Intell. Fuzzy Syst. 40 (2021), 2, 2041-2053. |
| Reference:
|
[19] Gasilov, N. A., Amrahov, S. E., Fatullayev, A. G.: On a solution of the fuzzy Dirichlet problem for the heat equation..Int. J. Thermal Sci. 103 (2016), 67-76. |
| Reference:
|
[20] Gasilov, N., Doğan, M., Arici, V.: Two-stage shortest path algorithm for solving optimal obstacle avoidance problem..IETE J. Res. 57 (2011), 3, 278-285. |
| Reference:
|
[21] Gilda, K. S., Satarkar, S. L.: Analytical overview of defuzzification methods..Int. J. Advance Res. Ideas Innova. Technol. 6 (2020), 2, 359-365. |
| Reference:
|
[22] Gu, X., Angelov, P. P., Shen, Q.: Semi-supervised fuzzily weighted adaptive boosting for classification..IEEE Trans. Fuzzy Systems 32 (2024), 4, 2318-2330. 10.1109/TFUZZ.2024.3349637 |
| Reference:
|
[23] Guan, X., Yu, F., Xu, H., Li, C., Guan, Y.: Flood risk assessment of urban metro system using random forest algorithm and triangular fuzzy number based analytical hierarchy process approach..Sustainable Cities Soc. 109 (2024), 105546. |
| Reference:
|
[24] Jang, J. S. R., Sun, C. T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence..Prentice-Hall, 1997. |
| Reference:
|
[25] Karaboga, D., Kaya, E.: Adaptive network based fuzzy inference system (ANFIS) training approaches: A comprehensive survey..Artif. Intell. Rev. 52 (2019), 2263-2293. 10.1007/s10462-017-9610-2 |
| Reference:
|
[26] Kartli, N., Bostanci, E., Guzel, M. S.: A new algorithm for optimal solution of fixed charge transportation problem..Kybernetika 59 (2023), 1, 45-63. MR 4567841, |
| Reference:
|
[27] Kartli, N., Bostanci, E., Guzel, M. S.: Heuristic algorithm for an optimal solution of fully fuzzy transportation problem..Computing 106 (2024), 10, 3195-3227. MR 4794582, |
| Reference:
|
[28] Kelley, P. R., Barry, R.: Sparse spatial autoregressions..Statisr. Probab. Lett. 33 (1997), 3, 291-297. 10.1016/S0167-7152(96)00140-X |
| Reference:
|
[29] Kondratenko, Y., Kozlov, O., Lysiuk, H., Kryvda, V., Maksymova, O.: Fuzzy automatic control of the pyrolysis process for the municipal solid waste of variable composition..J. Automat. Mobile Robotics Intell. Systems 16 (2022), 1, 83-94. |
| Reference:
|
[30] Kusumadewi, S., Rosita, L., Wahyuni, E. G.: Selection of aggregation function in fuzzy inference system for metabolic syndrome..Int. J. Advanced Sci. Engrg. Inform. Technol. 12 (2022), 5, 2140. |
| Reference:
|
[31] Kusumadewi, S., Rosita, L., Wahyuni, E. G.: Fuzzy linear regression based on a hybrid of fuzzy C-means and the fuzzy inference system for predicting serum iron levels in patients with chronic kidney disease..Expert Syst. Appl. 227 (2023), 120314. |
| Reference:
|
[32] Li, G., Hu, X., Chen, S., Chang, K., Li, P., Wang, Y.: Peak load forecasting using grammatical evolution-based fuzzy regression approach..Int. J. Comput. Commun. Control 19 (2024), 4, 6611. |
| Reference:
|
[33] Mallick, A. K., Das, A.: An analytical survey of defuzzification techniques..In: 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), Kuala Lumpur 2021. pp. 1-6. |
| Reference:
|
[34] Mei, Z., Zhao, T., Xie, X.: Hierarchical fuzzy regression tree: A new gradient boosting approach to design a TSK fuzzy model..Inform. Sci. 652 (2024), 119740. 10.1016/j.ins.2023.119740 |
| Reference:
|
[35] Mert, A.: Shannon entropy-based approach for calculating values of WABL parameters..J. Taibah Univ. Sci. 14 (2020), 1, 1100-1109. 10.1080/16583655.2020.1804157 |
| Reference:
|
[36] Nachaoui, M., Nachaoui, A., Shikhlinskaya, R. Y., Elmoufidi, A.: An improved hybrid defuzzification method for fuzzy controllers..Statist. Optim. Inform. Comput. 11 (2023), 1, 29-43. 10.19139/soic-2310-5070-1706 |
| Reference:
|
[37] Namgung, H., Ohn, S. W.: Fuzzy inference and sequence model-based collision risk prediction system for stand-on vessel..Sensors 22 (2022), 13. |
| Reference:
|
[38] Nasiboglu, R., Abdullayeva, R.: Analytical formulations for the level based weighted average value of discrete trapezoidal fuzzy numbers..Int. J. Soft Comput. (IJSC) 9 (2018), 2/3, 1-15. 10.5121/ijsc.2018.9301 |
| Reference:
|
[39] Nasiboglu, R., Akdogan, A.: Estimation of the second hand car prices from data extracted via web scraping techniques..J. Modern Technol. Engrg. 5 (2020), 2, 157-166. |
| Reference:
|
[40] Nasiboglu, R., Erten, Z. T.: A new model to determine the hierarchical structure of the wireless sensor networks..Turkish J. Electr. Engrg. Computer Sci. 27 (2019), 6, 4023-4037. 10.3906/elk-1811-142 |
| Reference:
|
[41] Nasiboglu, R., Nasibov, E.: FyzzyGBR - A gradient boosting regression software with fuzzy target values..Software Impacts 14 (2022), 100430. 10.1016/j.simpa.2022.100430 |
| Reference:
|
[42] Nasiboglu, R., Nasibov, E.: WABL method as a universal defuzzifier in the fuzzy gradient boosting regression model..Expert Syst. Appl. 212 (2023), 118771. 10.1016/j.eswa.2022.118771 |
| Reference:
|
[43] Nasiboglu, R.: A novel fuzzy inference model with rule-based defuzzification approach..J. Modern Technol. Engrg. 7 (2022), 2, 124-133. |
| Reference:
|
[44] Nasiboglu, R.: An approach to solution of verbal stated mathematical problems..J. Modern Technol. Engrg. 5 (2020), 1, 25-35. |
| Reference:
|
[45] Nasiboglu, R.: Analysis of different approaches to regression problem with fuzzy information..J. Modern Technol. Engrg. 7 (2022), 3, 187-198. |
| Reference:
|
[46] Nasibov, E. N., Kinay, A. O.: An iterative approach for estimation of student performances based on linguistic evaluations..Inform. Sci. 179 (2009), 5, 688-698. |
| Reference:
|
[47] Nasibov, E. N., Mert, A.: On methods of defuzzification of parametrically represented fuzzy numbers..Automat. Control Computer Sci. 41 (2007), 265-273. |
| Reference:
|
[48] Pourabdollah, A., Mendel, J. M., John, R. I.: Alpha-cut representation used for defuzzification in rule-based systems..Fuzzy Sets Syst. 399 (2020), 110-132. MR 4154438 |
| Reference:
|
[49] Ramly, N., Rusiman, M. S., Nasibov, E., Nasiboglu, R.: The comparison of fuzzy regression approaches with and without clustering method in predicting manufacturing income..J. Advanced Res. Appl. Sci. Engrg. Technol. 46 (2024), 1, 218-236. 10.37934/araset.46.1.218236 |
| Reference:
|
[50] Rashidi, S., Xu, W., Lin, D., Turpin, A., Kulik, L., Ehinger, K.: An active foveated gaze prediction algorithm based on a Bayesian ideal observer..Pattern Recogn. 143 (2023), 109694. |
| Reference:
|
[51] Riman, C. F., Abi-Char, P. E.: Fuzzy logic control for mobile robot navigation in automated storage..Int. J. Mechan. Engrg. Robotics Res. 12 (2023), 5, 313-323. 10.18178/ijmerr.12.5.313-323 |
| Reference:
|
[52] Savaş, S. K., Nasibov, E. N.: A fuzzy ID3 induction for linguistic data sets..Int. J. Intell. Syst. 33 (2018), 858-878. |
| Reference:
|
[53] Samet, R., Amrahov, S. E., Ziroğlu, A. H.: Fuzzy Rule-based image segmentation technique for rock thin section images..In: 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), Istanbul 2012. pp. 402-406. |
| Reference:
|
[54] Tian, Y., Nie, G., Tian, H., Cui, Q.: Impact of endpoint structure attributes on local information algorithms based on link prediction..Computing 105 (2023), 1, 115-129. MR 4530130, 10.1007/s00607-022-01115-z |
| Reference:
|
[55] Vafakhah, M., Loor, S. M. H., Pourghasemi, H., Katebikord, A.: Comparing performance of random forest and adaptive neuro-fuzzy inference system data mining models for flood susceptibility mapping..Arabian J. Geosci. 13 (2020), 1-16. |
| Reference:
|
[56] Wahba, M., Essam, R., El-Rawy, M., Al-Arifi, N., Abdalla, F., Elsadek, W. M.: Forecasting of flash flood susceptibility mapping using random forest regression model and geographic information systems..Heliyon 10 (2024), 13, e33982. |
| Reference:
|
[57] Yagiz, S., Gokceoglu, C.: Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness..Expert Systems Appl. 37 (2010), 2265-2272. 10.1016/j.eswa.2009.07.046 |
| Reference:
|
[58] Yazid, E., Garratt, M., Santoso, F.: Position control of a quadcopter drone using evolutionary algorithms-based self-tuning for first-order Takagi-Sugeno-Kang fuzzy logic autopilots..Appl. Soft Comput. 78 (2019), 373-392. |
| Reference:
|
[59] Yıldırım, H. B., Kullu, K., Amrahov, S. E.: A graph model and a three-stage algorithm to aid the physically disabled with navigation..Univ. Acces Inform. Soc. 23 (2024), 2, 901-911. |
| Reference:
|
[60] Zhang, H., Hu, X., Zhu, X., Liu, X., Pedrycz, W.: Application of gradient boosting in the design of fuzzy rule-based regression models..IEEE Trans. Knowl. Data Engrg. 36 (2024), 5621-5632. |
| Reference:
|
[61] Zhang, Q., Yao, Y., Kong, J., Ma, X., Zhu, H.: A new GNSS TEC neural network prediction algorithm with the data fusion of physical observation..IEEE Trans. Geosci. Remote Sensing 61 (2023), 1-12. |
| Reference:
|
[62] Zhu, X., Hu, X., Yang, L., Pedrycz, W., Li, Z.: A development of fuzzy rule-based regression models through using decision trees..IEEE Trans. Fuzzy Systems 32 (2024), 5, 2976-2986. |
| Reference:
|
[63] Zimmermann, K.: Combination of t-norms and their conorms..Kybernetika 59 (2023), 4, 527-536. MR 4660376, |
| . |
