Feature Imputation using Neutrosophic Set Theory in Machine Learning Regression Context

Authors

  • Yamen El Touati Department of Computer Science, Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia
  • Walid Abdelfattah Department of Mathematics, College of Arts and Science, Northern Border University, Saudi Arabia
Volume: 14 | Issue: 2 | Pages: 13688-13694 | April 2024 | https://doi.org/10.48084/etasr.7052

Abstract

The prediction context of machine learning aims to discern the underlying patterns that dictate the characteristics to forecast the output. This prediction lacks precision when the input data is not accurate or precise. This study focuses on feature imputation through the application of the neutrosophic set theory. The primary concept involves substituting feature data, which may have accuracy and correctness issues, with neutrosophic variables considering the degrees of truth, indeterminacy, and falsity to produce more precise and resilient predictions. The proposed method was implemented in a specific case study, and the results are analyzed.

Keywords:

neutrosophic set theory, machine learning, prediction, feature imputation

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References

M. I. Jordan and T. M. Mitchell, "Machine learning: Trends, perspectives, and prospects," Science, vol. 349, no. 6245, pp. 255–260, Jul. 2015.

R. Trinchero and F. Canavero, "Machine Learning Regression Techniques for the Modeling of Complex Systems: An Overview," IEEE Electromagnetic Compatibility Magazine, vol. 10, no. 4, pp. 71–79, 2021.

C. J. M. Maas and J. J. Hox, "Robustness issues in multilevel regression analysis," Statistica Neerlandica, vol. 58, no. 2, pp. 127–137, 2004.

Y. E. Touati, "Deadline Verification for Web Services Using Timed Automata," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 8013–8016, Feb. 2022.

U. Khan, K. Khan, F. Hassan, A. Siddiqui, and M. Afaq, "Towards Achieving Machine Comprehension Using Deep Learning on Non-GPU Machines," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4423–4427, Aug. 2019.

B. Trstenjak, D. Donko, and Z. Avdagic, "Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model," Engineering, Technology & Applied Science Research, vol. 6, no. 6, pp. 1212–1216, Dec. 2016.

R. Chen and I. C. Paschalidis, "A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization," Journal of Machine Learning Research, vol. 19, no. 13, pp. 1–48, 2018.

E. Hüllermeier, "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization," International Journal of Approximate Reasoning, vol. 55, no. 7, pp. 1519–1534, Oct. 2014.

F. Smarandache, A Unifying Field in Logics: Neutrosophic Logic. American Research Press, 1999.

F. Smarandache, A Unifying Field in Logics. Neutrosophy: Neutrosophic Probability, Set and Logic. American Research Press, 1999.

F. Smarandache and S. Pramanik, New Trends in Neutrosophic Theory and Applications. Pons Editions, 2016.

H. Wang, Florentin Smarandache, Y. Zhang, and R. Sunderraman, "Single valued neutrosophic sets," in Collected Papers. Volume XIV: Neutrosophics and other topics, Global Knowledge, 2022.

C. H. Wang, C. C. Chuang, and C. C. Tsai, "A fuzzy DEA–Neural approach to measuring design service performance in PCM projects," Automation in Construction, vol. 18, no. 5, pp. 702–713, Aug. 2009.

I. Deli and Y. Şubaş, "A ranking method of single valued neutrosophic numbers and its applications to multi-attribute decision making problems," International Journal of Machine Learning and Cybernetics, vol. 8, no. 4, pp. 1309–1322, Aug. 2017.

W. Abdelfattah, "Data envelopment analysis with neutrosophic inputs and outputs," Expert Systems, vol. 36, no. 6, 2019, Art. no. e12453.

M. Abdel-Basset, M. Mohamed, Y. Zhou, and I. Hezam, "Multi-criteria group decision making based on neutrosophic analytic hierarchy process," Journal of Intelligent & Fuzzy Systems, vol. 33, no. 6, pp. 4055–4066, Jan. 2017.

M. Abdel-Basset, M. Mohamed, A. N. Hussien, and A. K. Sangaiah, "A novel group decision-making model based on triangular neutrosophic numbers," Soft Computing, vol. 22, no. 20, pp. 6629–6643, Oct. 2018.

M. Abdel-Basset, M. Mohamed, and F. Smarandache, "An Extension of Neutrosophic AHP–SWOT Analysis for Strategic Planning and Decision-Making," Symmetry, vol. 10, no. 4, Apr. 2018, Art. no. 116.

G. A. F. Seber and A. J. Lee, Linear Regression Analysis. John Wiley & Sons, 2012.

G. C. McDonald, "Ridge regression," WIREs Computational Statistics, vol. 1, no. 1, pp. 93–100, 2009.

M. Awad and R. Khanna, "Support Vector Regression," in Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, M. Awad and R. Khanna, Eds. Berkeley, CA, USA: Apress, 2015, pp. 67–80.

V. Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica-Olmo, and M. Chica-Rivas, "Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines," Ore Geology Reviews, vol. 71, pp. 804–818, Dec. 2015.

S. Peter, F. Diego, F. A. Hamprecht, and B. Nadler, "Cost efficient gradient boosting," in Advances in Neural Information Processing Systems, 2017, vol. 30.

Elai, "Elaishalev/Countries_Happiness." Mar. 17, 2020, [Online]. Available: https://github.com/Elaishalev/Countries_Happiness.

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How to Cite

[1]
Y. El Touati and W. Abdelfattah, “Feature Imputation using Neutrosophic Set Theory in Machine Learning Regression Context”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13688–13694, Apr. 2024.

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