A FUZZINNESS PARAMETER OPTIMIZATION METHOD TO EXTRACT THE OPTIMAL SET OF LINGUISTIC SUMMARIES FROM NUMERIC DATA

Phạm Đình Phong; Phạm Thị Lan; Trần Xuân Thanh

doi:10.34238/tnu-jst.9824

A FUZZINNESS PARAMETER OPTIMIZATION METHOD TO EXTRACT THE OPTIMAL SET OF LINGUISTIC SUMMARIES FROM NUMERIC DATA

About this article

Received: 01/03/24 Revised: 28/03/24 Published: 29/03/24

Authors

1. Pham Dinh Phong , University of Transport and Comminications
2. Pham Thi Lan, Hanoi National University of Education
3. Tran Xuan Thanh, 1) East Asia University of Technology, 2) Graduate University of Science and Technology, VAST

Abstract

Extracting a set of linguistic summaries from numeric data aims to produce summary sentences expressed in natural language that describe the hidden knowledge in the numeric dataset. A number of genetic algorithm models have been proposed to extract the optimal set of linguistic summaries, in which the algorithm model for extracting the set of linguistic summaries ensures the interpretability of the content of the summary sentences by applying genetic algorithm with greedy strategy gives quite good results. However, the determination of fuzziness parameter values of the algorithm model depends on the expert's intuition. In this paper, a method to optimize the fuzziness parameter values to improve the quality of the set of linguistic summaries extracted from numeric data is proposed. Experimental results with the creep database show that with the optimized fuzziness parameter values, the quality of the extracted set of linguistic summaries is better on three measures: fitness function value, average truth value and number of sentences with linguistic quantifier greater than a half.

Keywords

Linguistic summary; Hegde algebras; Interpretability; Multi-semantic structure; Particle swarm optimization

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References

[1] R. R. Yager, "A new approach to the summarization of data," Information Sciences, vol. 28, no. 1, pp. 69-86, 1982.

[2] J. Kacprzyk, R. R. Yager, and S. Zadrożny, "A fuzzy logic based approach to linguistic summaries of databases," International Journal of Applied Mathematics and Computer Science, vol. 10, no. 4, pp. 813-834, 2000.

[3] J. Kacprzyk and S. Zadrożny, "Linguistic database summaries and their protoforms: towards natural language based knowledge discovery tools," Information Sciences, vol. 173, no. 4, pp. 281-304, 2005.

[4] C. A. D. Díaz, R. B. Pérez, and E. V. Morales, "Using Linguistic Data Summarization in the study of creep data for the design of new steels," in 11th International Conference on Intelligent Systems Design and Applications (ISDA), 2011, pp. 160-165.

[5] T. Altintop, R. R. Yager, D. Akay, F. E. Boran, and M. Ünal, "Fuzzy Linguistic Summarization with Genetic Algorithm: An Application with Operational and Financial Healthcare Data," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 25, no. 04, pp. 599-620, 2017.

[6] R. J. Almeida, M.-J. Lesot, B. Bouchon-Meunier, U. Kaymak, and G. Moyse, "Linguistic summaries of categorical time series for septic shock patient data," Fuzz-IEEE 2013-IEEE International Conference on Fuzzy Systems, Hyderabad, India. IEEE, Jul. 2013, pp.1-8.

[7] J. Kacprzyk and R. R. Yager, "Linguistic summaries of data using fuzzy logic," International Journal of General System, vol. 30, no. 2, pp. 133-154, 2001.

[8] M. D. Peláez-Aguilera, M. Espinilla, M. R. F. Olmo, and J. Medina, "Fuzzy linguistic protoforms to summarize heart rate streams of patients with ischemic heart disease," Complexity, vol. 2019, pp. 1-11, 2019.

[9] A. Duraj, P. S. Szczepaniak, and L. Chomatek, "Intelligent Detection of Information Outliers Using Linguistic Summaries with Non-monotonic Quantifiers," International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2020, pp. 787-799.

[10] A. Jain, M. Popescu, J. Keller, M. Rantz, and B. Markway, "Linguistic summarization of in-home sensor data," Journal of biomedical informatics, vol. 96, 2019, Art. no. 103240.

[11] A. Wilbik, I. Vanderfeesten, D. Bergmans, S. Heines, and W. van Mook, "Linguistic summaries for compliance analysis of a glucose management clinical protocol," IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018, pp. 1-7.

[12] F. E. Boran and D. Akay, "A generic method for the evaluation of interval type-2 fuzzy linguistic summaries," IEEE transactions on cybernetics, vol. 44, no. 9, pp. 1632-1645, 2013.

[13] C. Donis-Diaz, R. Bello, and J. Kacprzyk, "Linguistic data summarization using an enhanced genetic algorithm," Technical Transactions – Automatic Control, vol. 2013, no. 2, pp. 3-12, 2013.

[14] C. Donis-Diaz, A. Muro, R. Bello-Pérez, and E. V. Morales, "A hybrid model of genetic algorithm with local search to discover linguistic data summaries from creep data," Expert Systems with Applications, vol. 41, no. 4, pp. 2035-2042, 2014.

[15] T. L. Pham, C. H. Nguyen, and D. P. Pham, “Extracting an optimal set of linguistic summaries using genetic algorithm combined with greedy strategy,” Journal on Information Technologies & Communications, vol. 2020, no. 2, pp. 75-87, 2020.

[16] C. H. Nguyen, T. S. Tran, and D. P. Pham, "Modeling of a semantics core of linguistic terms based on an extension of hedge algebra semantics and its application," Knowledge-Based Systems, vol. 67, pp. 244-262, 2014.

[17] C. H. Nguyen, T. L. Pham, T. N. Nguyen, C. H. Ho, and T. A. Nguyen, "The linguistic summarization and the interpretability, scalability of fuzzy representations of multilevel semantic structures of word-domains," Microprocessors and Microsystems, vol. 81, 2021, Art. no. 103641.

[18] J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” Proceedings of the IEEE International Conference on Neural Networks, Piscataway, New Jersey. IEEE Service Center, 1995, pp. 1942-1948.

DOI: https://doi.org/10.34238/tnu-jst.9824

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