Exploring the synergistic effects of hybrid grid partitioning and rough set method for fuzzy rule generation in dataset classification
DOI:
https://doi.org/10.35335/emod.v17i2.18Keywords:
Dataset classification, Fuzzy rule generation, Hybrid grid partitioning, Rough set method, Synergistic effectsAbstract
This research explores the synergistic effects of hybrid grid partitioning and the rough set method for fuzzy rule generation in dataset classification. The aim is to improve the accuracy and interpretability of the classification process. The rough set-based feature selection technique is employed to identify the most relevant features for classification, leading to a focused and informative feature subset. The hybrid grid partitioning approach combines clustering algorithms and grid-based methods to create an efficient grid structure, capturing the intrinsic data distribution. This enhances the representation and separation of data regions, improving classification accuracy. The generated fuzzy rule base provides interpretable decision rules, enabling domain experts to gain insights into the classification process. The proposed approach strikes a balance between accuracy and interpretability, making it valuable for various domains. However, limitations such as generalizability and scalability should be considered. Comparative analysis with existing methods and real-world case studies would further validate the effectiveness of the approach. Overall, this research contributes to the advancement of dataset classification and provides a novel integrated approach for accurate and interpretable classification.
References
Alhenawi, E., Al-Sayyed, R., Hudaib, A., & Mirjalili, S. (2022). Feature selection methods on gene expression microarray data for cancer classification: A systematic review. Computers in Biology and Medicine, 140, 105051.
Ali, A., Heneash, U., Hussein, A., & Eskebi, M. (2022). Predicting Pavement Condition Index Using Fuzzy Logic Technique. Infrastructures, 7(7), 91.
Asgarnezhad, R., Majeed, S. S. A., Abbas, Z. A., & Salman, S. S. (2022). An Effective Algorithm to Improve Recommender Systems using Evolutionary Computation Algorithms and Neural Network: Using Evolutionary Computation Algorithms and Neural Networks, an Effective Algorithm to Improve Recommender Systems. Wasit Journal of Computer and Mathematics Sciences, 1(1), 27–35.
Buabeng, A., Simons, A., Frempong, N. K., & Ziggah, Y. Y. (2022). Predictive Maintenance Model Based on Multisensor Data Fusion of Hybrid Fuzzy Rough Set Theory Feature Selection and Stacked Ensemble for Fault Classification. Mathematical Problems in Engineering, 2022.
Chen, Y.-C., Pal, N. R., & Chung, I.-F. (2011). An integrated mechanism for feature selection and fuzzy rule extraction for classification. IEEE Transactions on Fuzzy Systems, 20(4), 683–698.
Cheruku, R., Edla, D. R., Kuppili, V., & Dharavath, R. (2018). Rst-batminer: A fuzzy rule miner integrating rough set feature selection and bat optimization for detection of diabetes disease. Applied Soft Computing, 67, 764–780.
Citakoglu, H., & Demir, V. (2023). Developing numerical equality to regional intensity–duration–frequency curves using evolutionary algorithms and multi-gene genetic programming. Acta Geophysica, 71(1), 469–488.
Criado-Ramón, D., Ruiz, L. G. B., & Pegalajar, M. C. (2023). An Improved Pattern Sequence-Based Energy Load Forecast Algorithm Based on Self-Organizing Maps and Artificial Neural Networks. Big Data and Cognitive Computing, 7(2), 92.
Das, A. K., Sengupta, S., & Bhattacharyya, S. (2018). A group incremental feature selection for classification using rough set theory based genetic algorithm. Applied Soft Computing, 65, 400–411.
Dhal, P., & Azad, C. (2022). A comprehensive survey on feature selection in the various fields of machine learning. Applied Intelligence, 1–39.
Fisher, B., Turner, R. K., & Morling, P. (2009). Defining and classifying ecosystem services for decision making. Ecological Economics, 68(3), 643–653.
García-García, F., Corral, A., Iribarne, L., & Vassilakopoulos, M. (2020). Improving distance-join query processing with voronoi-diagram based partitioning in spatialhadoop. Future Generation Computer Systems, 111, 723–740.
García-García, F., Corral, A., Iribarne, L., & Vassilakopoulos, M. (2018). Voronoi-diagram based partitioning for distance join query processing in spatialhadoop. Model and Data Engineering: 8th International Conference, MEDI 2018, Marrakesh, Morocco, October 24–26, 2018, Proceedings 8, 251–267.
Gómez-Skarmeta, A. F., Valdés, M., Jiménez, F., & Marı́n-Blázquez, J. G. (2001). Approximative fuzzy rules approaches for classification with hybrid-GA techniques. Information Sciences, 136(1–4), 193–214.
Guo, L., Daly, E. M., Alkan, O., Mattetti, M., Cornec, O., & Knijnenburg, B. (2022). Building trust in interactive machine learning via user contributed interpretable rules. 27th International Conference on Intelligent User Interfaces, 537–548.
Hostetter, J. W., Abdelshiheed, M., Barnes, T., & Chi, M. (2023). A self-organizing neuro-fuzzy q-network: Systematic design with offline hybrid learning. Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 1248–1257.
Idris, N. F., Ismail, M. A., Mohamad, M. S., Kasim, S., Zakaria, Z., & Sutikno, T. (2022). Breast cancer disease classification using fuzzy-ID3 algorithm based on association function. IAES International Journal of Artificial Intelligence, 11(2), 448.
Ikotun, A. M., & Ezugwu, A. E. (2022). Boosting k-means clustering with symbiotic organisms search for automatic clustering problems. Plos One, 17(8), e0272861.
Jara, L., Ariza-Valderrama, R., Fernández-Olivares, J., González, A., & Pérez, R. (2022). Efficient inference models for classification problems with a high number of fuzzy rules. Applied Soft Computing, 115, 108164.
Jensen, R., & Shen, Q. (2007). Fuzzy-rough sets assisted attribute selection. IEEE Transactions on Fuzzy Systems, 15(1), 73–89.
Jensen, R., & Shen, Q. (2008). Computational intelligence and feature selection: rough and fuzzy approaches.
Jiang, Q., Zhou, X., Wang, R., Ding, W., Chu, Y., Tang, S., Jia, X., & Xu, X. (2022). Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey. Applied Soft Computing, 108835.
Joshi, C., Ranjan, R. K., & Bharti, V. (2022). A Fuzzy Logic based feature engineering approach for Botnet detection using ANN. Journal of King Saud University-Computer and Information Sciences, 34(9), 6872–6882.
Khan, F., Siva Prasad, B. V. V., Syed, S. A., Ashraf, I., & Ramasamy, L. K. (2022). An efficient, ensemble-based classification framework for big medical data. Big Data, 10(2), 151–160.
Lyngdoh, G. A., Zaki, M., Krishnan, N. M. A., & Das, S. (2022). Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning. Cement and Concrete Composites, 128, 104414.
Moshayedi, A. J., Roy, A. S., Kolahdooz, A., & Shuxin, Y. (2022). Deep Learning Application Pros And Cons Over Algorithm Deep Learning Application Pros And Cons Over Algorithm. EAI Endorsed Transactions on AI and Robotics, 1(1).
Nilashi, M., Abumalloh, R. A., Alrizq, M., Almulihi, A., Alghamdi, O. A., Farooque, M., Samad, S., Mohd, S., & Ahmadi, H. (2022). A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach. Mathematical Problems in Engineering, 2022.
Nivetha, S., & Inbarani, H. H. (2023). Novel Architecture for Image Classification Based on Rough Set. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 14(1), 1–38.
Pattaraintakorn, P., & Cercone, N. (2008). Integrating rough set theory and medical applications. Applied Mathematics Letters, 21(4), 400–403.
Priya, S., Selvakumar, S., & Velusamy, R. L. (2022). PaSOFuAC: Particle Swarm Optimization Based Fuzzy Associative Classifier for Detecting Phishing Websites. Wireless Personal Communications, 125(1), 755–784.
Rana, P., Rai, S. K., & Shrivastav, K. (n.d.). A Comparative analysis on Fuzzy systems-based Management Models.
Salamo, M., & Lopez-Sanchez, M. (2011). Rough set based approaches to feature selection for case-based reasoning classifiers. Pattern Recognition Letters, 32(2), 280–292.
Sanz, J. A., Bernardo, D., Herrera, F., Bustince, H., & Hagras, H. (2014). A compact evolutionary interval-valued fuzzy rule-based classification system for the modeling and prediction of real-world financial applications with imbalanced data. IEEE Transactions on Fuzzy Systems, 23(4), 973–990.
Schwaller, P., Vaucher, A. C., Laplaza, R., Bunne, C., Krause, A., Corminboeuf, C., & Laino, T. (2022). Machine intelligence for chemical reaction space. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(5), e1604.
Shapiro, A. F. (2002). The merging of neural networks, fuzzy logic, and genetic algorithms. Insurance: Mathematics and Economics, 31(1), 115–131.
Shelke, N., Chaudhury, S., Chakrabarti, S., Bangare, S. L., Yogapriya, G., & Pandey, P. (2022). An efficient way of text-based emotion analysis from social media using LRA-DNN. Neuroscience Informatics, 100048.
Subhashini, L., Li, Y., Zhang, J., & Atukorale, A. S. (2022). Integration of fuzzy logic and a convolutional neural network in three-way decision-making. Expert Systems with Applications, 202, 117103.
Tiwari, S., & Agarwal, S. (2023). Empirical analysis of chronic disease dataset for multiclass classification using optimal feature selection based hybrid model with spark streaming. Future Generation Computer Systems, 139, 87–99.
Turner, S. D., Dudek, S. M., & Ritchie, M. D. (2010). ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci. BioData Mining, 3, 1–18.
Udhaya Kumar, S., & Hannah Inbarani, H. (2017). PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task. Neural Computing and Applications, 28, 3239–3258.
Varshney, A. K., & Torra, V. (2023). Literature Review of the Recent Trends and Applications in Various Fuzzy Rule-Based Systems. International Journal of Fuzzy Systems, 1–24.
Wan, S., Zhao, Y., Wang, T., Gu, Z., Abbasi, Q. H., & Choo, K.-K. R. (2019). Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things. Future Generation Computer Systems, 91, 382–391.
Wang, G.-Y., Yao, Y.-Y., & Yu, H. (2009). A survey on rough set theory and applications. Chinese Journal of Computers, 32(7), 1229–1246.
Xue, G., Chang, Q., Wang, J., Zhang, K., & Pal, N. R. (2022). An adaptive neuro-fuzzy system with integrated feature selection and rule extraction for high-dimensional classification problems. IEEE Transactions on Fuzzy Systems.
Yang, C., Cheng, T., Li, S., Gu, X., & Yang, L. (2023). A novel partitioning method for the power grid restoration considering the support of multiple LCC-HVDC systems. Energy Reports, 9, 1104–1112.
Zhai, L.-Y., Khoo, L.-P., & Zhong, Z.-W. (2008). A rough set enhanced fuzzy approach to quality function deployment. The International Journal of Advanced Manufacturing Technology, 37, 613–624.
Zhai, L.-Y., Khoo, L.-P., & Zhong, Z.-W. (2009). A rough set based QFD approach to the management of imprecise design information in product development. Advanced Engineering Informatics, 23(2), 222–228.
Zhang, R., Yu, Y., Shetty, P., Song, L., & Zhang, C. (2022). Prboost: Prompt-based rule discovery and boosting for interactive weakly-supervised learning. ArXiv Preprint ArXiv:2203.09735.
Zhao, T., Cao, H., & Dian, S. (2022). A self-organized method for a hierarchical fuzzy logic system based on a fuzzy autoencoder. IEEE Transactions on Fuzzy Systems, 30(12), 5104–5115.
Zheng, B., Xu, J., Lee, W.-C., & Lee, D. L. (2006). Grid-partition index: a hybrid method for nearest-neighbor queries in wireless location-based services. The VLDB Journal, 15, 21–39.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Abubakullo Abubakullo, Aisyah Alesha
This work is licensed under a Creative Commons Attribution 4.0 International License.
