Optimizing Fuzzy Grid Partition Performance for Rule Generation in Dataset Classification
DOI:
https://doi.org/10.35335/emod.v14i1.26Keywords:
Fuzzy grid partitioning, Rule generation, Dataset classification, Optimization, PerformanceAbstract
This research focuses on optimizing fuzzy grid partitioning for rule generation in dataset classification. Fuzzy grid partitioning is a technique used to divide the feature space into fuzzy regions, which are subsequently used to generate rules for classification. However, the existing implementation of fuzzy grid partitioning suffers from limitations related to grid size determination, adaptability to data distributions, computational efficiency, and rule quality. To address these challenges, we formulate a mathematical problem and propose an optimization process that iteratively adjusts the grid size, performs fuzzy grid partitioning, generates rules, prunes them for relevance, and evaluates the resulting rule-based classifier. The optimization aims to find an optimal balance between accuracy and efficiency while ensuring that the generated rules meet the desired performance thresholds. Through a numerical example, we demonstrate the effectiveness of the optimization process, showcasing how it produces an accurate and efficient rule-based classifier. This research contributes to advancements in fuzzy grid partitioning for rule generation, improving the accuracy, efficiency, and interpretability of rule-based classifiers. It opens avenues for further investigation and refinement of the optimization techniques, enabling better dataset classification in various domains where transparent decision-making processes are essential.
References
Amudha, J., Radha, D., & Smitha, S. (2015). Analysis of fuzzy rule optimization models. Int J Eng Technol, 7(5), 1564-1570.
Benmouiza, K., & Cheknane, A. (2019). Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theoretical and Applied Climatology, 137, 31-43.
Chen, C. C. (2006). Design of PSO-based fuzzy classification systems. Journal of Applied Science and Engineering, 9(1), 63-70.
Chen, H. M., & Ho, S. Y. (2001, July). Designing an optimal evolutionary fuzzy decision tree for data mining. In Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation (pp. 943-950).
Chen, T., Shen, Q., Su, P., & Shang, C. (2016). Fuzzy rule weight modification with particle swarm optimisation. Soft Computing, 20, 2923-2937.
Chiang, J. H., & Hao, P. Y. (2004). Support vector learning mechanism for fuzzy rule-based modeling: a new approach. IEEE Transactions on Fuzzy systems, 12(1), 1-12.
Chiu, S. (1996, June). Method and software for extracting fuzzy classification rules by subtractive clustering. In Proceedings of North American Fuzzy Information Processing (pp. 461-465). IEEE.
Chiu, S. (1997). Extracting fuzzy rules from data for function approximation and pattern classification. Fuzzy Information Engineering: a guided tour of applications, 9.
Dehzangi, O., Zolghadri, M. J., Taheri, S., & Fakhrahmad, S. M. (2007, August). Efficient fuzzy rule generation: a new approach using data mining principles and rule weighting. In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) (Vol. 2, pp. 134-139). IEEE.
Gadaras, I., & Mikhailov, L. (2009). An interpretable fuzzy rule-based classification methodology for medical diagnosis. Artificial intelligence in medicine, 47(1), 25-41.
Gu, X., Zhang, C., & Ni, T. (2019). Feature selection and rule generation integrated learning for Takagi-Sugeno-Kang fuzzy system and its application in medical data classification. IEEE Access, 7, 169029-169037.
Guillaume, S. (2001). Designing fuzzy inference systems from data: An interpretability-oriented review. IEEE Transactions on fuzzy systems, 9(3), 426-443.
Guillaume, S., & Charnomordic, B. (2004). Generating an interpretable family of fuzzy partitions from data. IEEE transactions on fuzzy systems, 12(3), 324-335.
Ho, S. Y., Chen, H. M., Ho, S. J., & Chen, T. K. (2004). Design of accurate classifiers with a compact fuzzy-rule base using an evolutionary scatter partition of feature space. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(2), 1031-1044.
Hu, Y. C. (2006). Determining membership functions and minimum fuzzy support in finding fuzzy association rules for classification problems. Knowledge-Based Systems, 19(1), 57-66.
Hu, Y. C., Chen, R. S., & Tzeng, G. H. (2003). Finding fuzzy classification rules using data mining techniques. Pattern recognition letters, 24(1-3), 509-519.
Ibrahim, S., Chowriappa, P., Dua, S., Acharya, U. R., Noronha, K., Bhandary, S., & Mugasa, H. (2015). Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier. Medical & biological engineering & computing, 53, 1345-1360.
Ishibuchi, H., & Murata, T. (1997, July). Minimizing the fuzzy rule base and maximizing its performance by a multiobjective genetic algorithm. In Proceedings of 6th International Fuzzy Systems Conference (Vol. 1, pp. 259-264). IEEE.
Ishibuchi, H., & Nakashima, T. (1998, April). A study on generating fuzzy classification rules using histograms. In 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No. 98EX111) (Vol. 1, pp. 132-140). IEEE.
Ishibuchi, H., & Yamamoto, T. (2003). Trade-off between the number of fuzzy rules and their classification performance. Accuracy improvements in linguistic fuzzy modeling, 72-99.
Ishibuchi, H., & Yamamoto, T. (2005). Rule weight specification in fuzzy rule-based classification systems. IEEE transactions on fuzzy systems, 13(4), 428-435.
Ishibuchi, H., Nakashima, T., & Morisawa, T. (1997, September). Simple fuzzy rule-based classification systems perform well on commonly used real-world data sets. In 1997 Annual Meeting of the North American Fuzzy Information Processing Society-NAFIPS (Cat. No. 97TH8297) (pp. 251-256). IEEE.
Ishibuchi, H., Nakashima, T., & Murata, T. (1999). Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(5), 601-618.
Ishibuchi, H., Nakashima, T., & Nii, M. (2000). Fuzzy if-then rules for pattern classification. Fuzzy If-Then Rules in Computational Intelligence: Theory and Applications, 267-295.
Ishibuchi, H., Nakashima, Y., & Nojima, Y. (2011). Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning. Soft Computing, 15, 2415-2434.
Jahromi, M. Z., & Taheri, M. (2008). A proposed method for learning rule weights in fuzzy rule-based classification systems. Fuzzy sets and Systems, 159(4), 449-459.
Jain, R., & Abraham, A. (2004). A comparative study of fuzzy classification methods on breast cancer data. Australasian physical & engineering sciences in medicine, 27(4), 213-218.
Jiao, L., Denoeux, T., & Pan, Q. (2015). A hybrid belief rule-based classification system based on uncertain training data and expert knowledge. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(12), 1711-1723.
Kalia, H., Dehuri, S., Ghosh, A., & Cho, S. B. (2018). Surrogate-assisted multi-objective genetic algorithms for fuzzy rule-based classification. International Journal of Fuzzy Systems, 20, 1938-1955.
Kbir, M. A., Benkirane, H., Maalmi, K., & Benslimane, R. (2000). Hierarchical fuzzy partition for pattern classification with fuzzy if-then rules. Pattern Recognition Letters, 21(6-7), 503-509.
Kim, M. W., & Ryu, J. W. (2004). Optimized fuzzy classification for data mining. In Database Systems for Advanced Applications: 9th International Conference, DASFAA 2004, Jeju Island, Korea, March 17-19, 2003. Proceedings, 9 (pp. 582-593). Springer Berlin Heidelberg.
Kim, M. W., Ryu, J. W., Kim, S., & Lee, J. G. (2003). Optimization of fuzzy rules for classification using genetic algorithm. In Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference, PAKDD 2003, Seoul, Korea, April 30–May 2, 2003 Proceedings 7 (pp. 363-375). Springer Berlin Heidelberg.
Rasmani, K. A., & Shen, Q. (2006). Data-driven fuzzy rule generation and its application for student academic performance evaluation. Applied Intelligence, 25, 305-319.
Riza, L. S., Bergmeir, C., Herrera, F., & Benítez, J. M. (2015). frbs: Fuzzy rule-based systems for classification and regression in R. Journal of statistical software, 65, 1-30.
Roubos, J. A., Setnes, M., & Abonyi, J. (2003). Learning fuzzy classification rules from labeled data. Information sciences, 150(1-2), 77-93.
Şahi̇n, M. A., & Leblebi̇ci̇oğlu, K. (2014). Approximating the optimal mapping for weapon target assignment by fuzzy reasoning. Information Sciences, 255, 30-44.
Salleh, M. N. M., Talpur, N., & Talpur, K. H. (2018). A modified neuro-fuzzy system using metaheuristic approaches for data classification. Artificial intelligence–emerging trends and applications’.(Ed. MAA Fernandez) pp, 29-45.
Setnes, M., & Roubos, H. (2000). GA-fuzzy modeling and classification: complexity and performance. IEEE transactions on Fuzzy Systems, 8(5), 509-522.
Sheikhan, M., & Sharifi Rad, M. (2013). Gravitational search algorithm–optimized neural misuse detector with selected features by fuzzy grids–based association rules mining. Neural Computing and Applications, 23, 2451-2463.
Soua, B., Borgi, A., & Tagina, M. (2013). An ensemble method for fuzzy rule-based classification systems. Knowledge and information systems, 36, 385-410.
Stavrakoudis, D. G., Galidaki, G. N., Gitas, I. Z., & Theocharis, J. B. (2011). A genetic fuzzy-rule-based classifier for land cover classification from hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing, 50(1), 130-148.
Zhang, Y., Qian, X., Wang, J., & Gendeel, M. (2019). Fuzzy rule-based classification system using multi-population quantum evolutionary algorithm with contradictory rule reconstruction. Applied Intelligence, 49, 4007-4021.
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