Some Granular Computing Based Machine Learning Algorithms
محورهای موضوعی : Transactions on Fuzzy Sets and Systems
1 - Department of Mathematics, Assistant Professor of Jai Hind College, Jai Hind College, Churchgate, Mumbai 400020, India.
کلید واژه: Big data, Information granules, GrC, ML.,
چکیده مقاله :
Granular computing has emerged as a new computational method that is beneficial when dealing with large amounts of data. In recent years, several machine learning models based on the granular framework have been developed, outperforming traditional machine learning models. This article reviews some newly developed techniques in terms of granular framework settings.
Granular computing has emerged as a new computational method that is beneficial when dealing with large amounts of data. In recent years, several machine learning models based on the granular framework have been developed, outperforming traditional machine learning models. This article reviews some newly developed techniques in terms of granular framework settings.
[1] Zadeh LA. Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Computing. 1998; 2(1): 23-25. DOI: https://doi.org/10.1007/s005000050030
[2] Chen D, Xu W, Li J. Granular computing in machine learning. Granular Computing. 2019; 4: 299-300. DOI: https://doi.org/10.1007/s41066-018-00146-2
[3] Mienye ID, Sun Y. A survey of ensemble learning: Concepts, algorithms, applications, and prospects. IEEE Access. 2022; 10: 99129-99149. DOI: https://doi.org/10.1109/ACCESS.2022.3207287
[4] Liu H, Zhang L. Advancing ensemble learning performance through data transformation and classifiers fusion in granular computing context. Expert Systems with Applications. 2019; 131: 20-29. DOI: https://doi.org/10.1016/j.eswa.2019.04.051
[5] Yao JT, Yao YY. Granular computing approach to machine learning. In: Wang L, Halgamuge SK, Yao X. (eds.) Fsdk 2002: Proceedings of the 1st International Conference on Fuzzy Systems and Knowledge Discovery: Computational Intelligence for the E-Age, FSDK 2002, 18-22 November 2002, Singapore. Orchid Country Club; 2002. p.732-736. https://www.semanticscholar.org/paper/A-Granular-ComputingApproach-to-Machine-Learning-Yao-Yao/0186028b4aaf265d8590dcb0890f900ee4095b0a
[6] Yao YY. On modeling data mining with granular computing. In: 25th Annual International Computer Software and Applications Conference. COMPSAC 2001, 08-12 October 2001, Chicago, IL, USA. IEEE; 2001. p.638-643. DOI: https://doi.org/10.1109/CMPSAC.2001.960680
[7] Allison R. Visualizing Data with SAS R. : Selected Topics. Cary NC USA: SAS Institute Inc; 2017. https://support.sas.com/content/dam/SAS/support/en/books/free-books/vds.pdf
[8] Laney D. 3D Data Management: Controlling Data Volume, Velocity, and Variety (). META Group 2001.
https://www.bibsonomy.org/bibtex/742811cb00b303261f79a98e9b80bf49 [Accessed 1st December 2024].
[9] Ishwarappa, Anuradha J. A brief introduction on Big Data 5Vs characteristics and Hadoop technology. Procedia Computer Science. 2015; 48: 319-324. DOI: https://doi.org/10.1016/j.procs.2015.04.188
[10] Webster M. Granule: Merriam-Webster.com Dictionary. https://www.merriamwebster.com/dictionary/granule [Accessed 26th October 2024].
[11] Keet CM. Abstraction. In: Dubitzky W, Wolkenhauer O, Cho KH, Yokota H. (eds.) Encyclopedia of Systems Biology. New York: Springer; 2013. p.3-4. DOI: https://doi.org/10.1007/978-1-4419-9863-7
[12] Lin TY. Granular Computing: Practices, Theories, and Future Directions. In: Meyers R. (eds.) Encyclopedia of Complexity and Systems Science. Springer, New York, NY; 2009. p.4339-4355. DOI: https://doi.org/10.1007/978-0-387-30440-3 256
[13] Hu H, Shi Z. Machine learning as granular computing. In: 2009 IEEE International Conference on Granular Computing, Nanchang, China. IEEE; 2009. p.229-234. DOI: https://doi.org/10.1109/GRC.2009.5255125
[14] Zadeh LA. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems. 1997; 90(2): 111-127. DOI: https://doi.org/10.1016/S0165- 0114(97)00077-8
[15] Liu H, Cocea M. Granular Computing Based Machine Learning: A Big Data Processing Approach. Springer International Publishing AG; 2018. https://link.springer.com/book/10.1007/978-3-319-70058-8
[16] Tiwari VR. Developments in KD Tree and KNN searches. International Journal of Computer Applications. 2023; 185(17): 17-23. DOI: https://doi.org/10.5120/ijca2023922879
[17] Liu H, Gegov A, Cocea M. Nature and biology inspired approach of classification towards reduction of bias in machine learning. In: 2016 International Conference on Machine Learning and Cybernetics (ICMLC), 10-13 July 2016, Jeju, Korea (South). IEEE; 2016. p.588-593. DOI:
https://doi.org/10.1109/ICMLC.2016.7872953
[18] Liu H, Cocea M, Mohasseb A, Bader M. Transformation of discriminative single-task classification into generative multi-task classification in machine learning context. In: 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI), 46 February 2017, Doha, Qatar. IEEE; 2017. p.66-73. DOI: http://doi.org/10.1109/ICACI.2017.7974487
[19] Liu H, Cocea M. Fuzzy information granulation towards interpretable sentiment analysis. Granular Computing. 2017; 2: 289-302. DOI: https://doi.org/10.1007/s41066-017-0043-8
[20] Liu H, Cocea M. Semi-random partitioning of data into training and test sets in granular computing context. Granular Computing. 2017; 2: 357-386. DOI: https://doi.org/10.1007/s41066-017-0049-2
[21] Liu H, Cocea M. Granular computing-based approach of rule learning for binary classification. Granular Computing. 2019; 4: 275-283. DOI: https://doi.org/10.1007/s41066-018-0097-2
[22] Liu H, Gegov A, Cocea M. Collaborative rule generation: An ensemble learning approach. Journal of Intelligent and Fuzzy Systems. 2016; 30(4): 2277-2287. DOI: https://doi.org/10.3233/IFS-151997
[23] Behzadidoost R, Mahan F, Izadkhah H. Granular computing-based deep learning for text classification. Information Sciences. 2024; 119746. DOI: https://doi.org/10.1016/j.ins.2023.119746
[24] Khalif KMNK, Muhammad N, Aziz MKBM, Irawan MI, Iqbal M, Setiawan MN. Advancing machine learning for identifying cardiovascular disease via granular computing. IAES International Journal of Artificial Intelligence. 2024; 13(2): 2433-2440. DOI: https://doi.org/10.11591/ijai.v13.i2.pp2433-2440
