DATA INTEGRATION IN A MULTI-TYPE DATA ENVIRONMENT
Keywords:
APMDE, ANFIS, multi-type data, multi-type data environment, data integrationAbstract
The digital revolution in numerous fields of life has caused the rapid increase of multi data-types, therefore more demand for a suitable and efficient data integration. In multi-type data environment this paper presents a systematic way of data integration using the APMDE (Ability prediction in a multitype data environment) software tool. Built to process numerical, categorical as well as time series data, this tool combines advanced models including ANFIS (Adaptive Neuro-Fuzzy Inference System) and genetic algorithms for potent predictive performance. We describe how data integration is performed, its issues and difficulties, as well the algorithmic basis behind the impact a system can accomplish with examples of how to solve different tasks mathematically reducing them into several steps and providing formulas or algorithms.
References
Egamberdiyev E. ANSAMBL USULI YORDAMIDA MULTITIPLI MA'LUMOTLARNI QAYTA ISHLASH //Sun’iy Intellekt Nazariyasi va Amaliyoti: Tajribalar, Muammolar va Istiqbollari. – 2024. – С. 27-30.
Mo’minov B., Egamberdiyev E. MULTITIPLI MA’LUMOTLARGA INTELLEKTUAL ISHLOV BERISH //DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE. – 2023. – Т. 1. – №. 2. – С. 43-46.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.
Hall, D. L., & Llinas, J. (2001). Handbook of Multisensor Data Fusion. CRC Press.
Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685.
Kumar, A., Singh, P., & Gupta, R. (2020). Ensemble learning for multi-type data integration: Challenges and solutions. Journal of Machine Learning Research, 21(120), 1-26.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Mitchell, M. (1998). An Introduction to Genetic Algorithms. MIT Press.
Nguyen, Q. V., Sun, T., & Yang, M. (2018). Multi-view learning: A comprehensive survey. Data Mining and Knowledge Discovery, 32(3), 529–577.
Nikravesh, M., & Azadeh, A. (2020). Genetic algorithm optimization of ANFIS models in industrial systems. Applied Soft Computing, 88, 106066.
Pennington, J., Socher, R., & Manning, C. (2014). GloVe: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532-1543.
Rao, S., & Srinivas, K. (2021). A review of ANFIS in the integration of multi-type data: Methods and applications. International Journal of Fuzzy Systems, 23(4), 877-892.
Zadeh, L. A. (2005). Fuzzy Logic and Soft Computing. Springer.
