Feature Imputation using Neutrosophic Set Theory in Machine Learning Regression Context
Received: 11 February 2024 | Revised: 28 February 2024 | Accepted: 11 March 2024 | Online: 14 March 2024
Corresponding author: Yamen El Touati
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 imputationDownloads
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.
Downloads
How to Cite
License
Copyright (c) 2024 Yamen El Touati, Walid Abdelfattah
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
