Complex Data Set Problem Solving With Hybrid Grid Partition And Rought Set Method For Fuzzy Rule Generation As A Solution
DOI:
https://doi.org/10.35335/emod.v14i3.34Keywords:
Complex data set, Hybrid grid partitioning, Rough set method, Fuzzy rule generation, Problem-solvingAbstract
This research proposes a novel approach that combines hybrid grid partitioning and the rough set method for fuzzy rule generation to address the challenges posed by complex data set problem-solving. The approach aims to improve data representation, handle uncertainty, identify relevant features, extract dependency rules, and generate accurate fuzzy rules. The hybrid grid partitioning technique probabilistically assigns data points to cells based on local density, enhancing data representation and capturing variations in data density. The rough set method is applied within each cell to identify relevant features and extract dependency rules, considering the uncertainty and incompleteness present in the data. Fuzzy logic is incorporated to generate linguistically interpretable fuzzy rules that capture the complex relationships within the data set. The proposed approach offers an effective solution for complex data analysis, enabling enhanced decision-making, prediction accuracy, and understanding across various domains. However, the approach has limitations, including sensitivity to parameter selection, computational complexity, assumption of independence, interpretability of fuzzy rules, and generalizability to diverse domains. Further research and refinement are necessary to address these limitations and enhance the approach's performance and applicability. Overall, this research contributes to the field of complex data analysis by providing a comprehensive approach for problem-solving, with the potential to advance decision-making and understanding in complex data sets.
References
Athinarayanan, S., Srinath, M. V., & Kavitha, R. (2017). Multi Class Cervical Cancer Classification by using ERSTCM, EMSD and CFE Methods Based Texture Features and Fuzzy Logic Based Hybrid Kernel Support Vector Machine Classifier. IOSR Journal of Computer Engineering, 19(1), 23-34.
Dastjerd, N. K., Sert, O. C., Ozyer, T., & Alhajj, R. (2019). Fuzzy classification methods based diagnosis of Parkinson’s disease from speech test cases. Current aging science, 12(2), 100-120.
Duan, J. C., & Chung, F. L. (2001). Cascaded fuzzy neural network model based on syllogistic fuzzy reasoning. IEEE Transactions on Fuzzy Systems, 9(2), 293-306.
Feng, H. M., & Wong, C. C. (2007). Fewer hyper-ellipsoids fuzzy rules generation using evolutional learning scheme. Cybernetics and Systems, 39(1), 19-44.
Frattale Mascioli, F. M., Rizzi, A., Panella, M., & Bettiol, C. (2007, July). Optimization of hybrid electric cars by neuro-fuzzy networks. In International Workshop on Fuzzy Logic and Applications (pp. 253-260). Berlin, Heidelberg: Springer Berlin Heidelberg.
Gorzałczany, M. B., & Rudziński, F. (2016). A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability. Applied Soft Computing, 40, 206-220.
Hassan, S. Z., & Verma, B. (2007, October). A hybrid data mining approach for knowledge extraction and classification in medical databases. In Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007) (pp. 503-510). IEEE.
Huang, M., Ma, Y., Wan, J., & Chen, X. (2015). A sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process. Applied Soft Computing, 27, 1-10.
Huang, X., Shi, F., Gu, W., & Chen, S. (2009). SVM-based fuzzy rules acquisition system for pulsed GTAW process. Engineering Applications of Artificial Intelligence, 22(8), 1245-1255.
Hüllermeier, E. (2005). Fuzzy methods in machine learning and data mining: Status and prospects. Fuzzy sets and Systems, 156(3), 387-406.
Khashei, M., Hamadani, A. Z., & Bijari, M. (2012). A fuzzy intelligent approach to the classification problem in gene expression data analysis. Knowledge-Based Systems, 27, 465-474.
Kostek, B. (2013). Soft computing in acoustics: applications of neural networks, fuzzy logic and rough sets to musical acoustics (Vol. 31). Physica.
Kumar, P. R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques–A review. European journal of operational research, 180(1), 1-28.
Lee, H. E., Park, K. H., & Bien, Z. Z. (2008). Iterative fuzzy clustering algorithm with supervision to construct probabilistic fuzzy rule base from numerical data. IEEE Transactions on Fuzzy Systems, 16(1), 263-277.
Mohanty, R., Ravi, V., & Patra, M. R. (2010). The application of intelligent and soft-computing techniques to software engineering problems: a review. International Journal of Information and Decision Sciences, 2(3), 233-272.
Nanda, N. B., & Parikh, A. (2019). Hybrid approach for network intrusion detection system using random forest classifier and rough set theory for rules generation. In Advanced Informatics for Computing Research: Third International Conference, ICAICR 2019, Shimla, India, June 15–16, 2019, Revised Selected Papers, Part II 3 (pp. 274-287). Springer Singapore.
Nilashi, M., Ahmadi, H., Shahmoradi, L., Ibrahim, O., & Akbari, E. (2019). A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. Journal of infection and public health, 12(1), 13-20.
Poongothai, S., Dharuman, C., & Venkatesan, P. (2019, November). A comparative study of hybrid evolutionary based algorithms with machine learning classifiers for the prediction of medical database. In Journal of Physics: Conference Series (Vol. 1377, No. 1, p. 012026). IOP Publishing.
Poongothai, S., Dharuman, C., Venkatesan, P., & Campus, R. (2017). A comparison of fuzzy genetic and neuro genetic hybrid algorithm for the classification of diabetes disease. International Journal of Pure and Applied Mathematics, 113, 208-216.
Rajab, S., & Sharma, V. (2019). An interpretable neuro-fuzzy approach to stock price forecasting. Soft Computing, 23, 921-936.
Suganthi, L., Iniyan, S., & Samuel, A. A. (2015). Applications of fuzzy logic in renewable energy systems–a review. Renewable and sustainable energy reviews, 48, 585-607.
Zaman, M., & Hassan, A. (2019). Improved statistical features-based control chart patterns recognition using ANFIS with fuzzy clustering. Neural Computing and Applications, 31(10), 5935-5949.
Zhou, S. M., & Gan, J. Q. (2007). Constructing L2-SVM-based fuzzy classifiers in high-dimensional space with automatic model selection and fuzzy rule ranking. IEEE Transactions on Fuzzy Systems, 15(3), 398-409.
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