ESTIMATION OF MAXIMUM LIKELIHOOD WEIGHTED LOGISTIC REGRESSION USING GENETIC ALGORITHM (CASE STUDY: INDIVIDUAL WORK STATUS IN MALANG CITY)

  • Dahlia Gladiola Rurina Menufandu Departemen of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
  • Rahma Fitriani Departemen of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
  • Eni Sumarminingsih Departemen of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss1pp0487-0494
Keywords: Genetic algorithm, Weighted logistic regression

Abstract

Weighted Logistic Regression (WLR) is a method used to overcome imbalanced data or rare events by using weighting and is part of the development of a simple logistic regression model. Parameter estimation of the WLR model uses Maximum Likelihood estimation. The maximum likelihood parameter estimator value is obtained using an optimization approach.  The Genetic algorithm is an optimization computational algorithm that is used to optimize the estimation of model parameters. This study aims to estimate the Maximum Likelihood Weighted Logistic Regression with the applied genetic algorithm and determine the significant variables that affect the working status of individuals in Malang City. The data used is the result of data collection from the National Labor Force Survey of Malang City in 2020. The results of the analysis show that the variable education completed and the number of household members has a significant effect on individual work status in Malang City.

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References

X. Zou, Y. Hu, Z. Tian, and K. Shen, “Logistic Regression Model Optimization and Case Analysis,” Proc. IEEE 7th Int. Conf. Comput. Sci. Netw. Technol. ICCSNT 2019, pp. 135–139, October 19-20 2019.

M. Maalouf, D. Homouz, and T. B. Trafalis, “Logistic regression in large rare events and imbalanced data: A performance comparison of prior correction and weighting methods,” Comput. Intell., vol. 34, no. 1, pp. 161–174, February 2017.

R. K. Vinayak, W. Kong, G. Valiant, and S. Kakade, “Maximum likelihood estimation for learning populations of parameters,” 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019-June, pp. 11217–11226, February 2019.

D. E. A. Sulasih, S. Wulan Purnami, and S. Puteri Rahayu, “the Theoretical Study of Rare Event Weighted Logistic Regression for Classification of Imbalanced Data,” Int. Conf. Sci. Technol. Humanit., pp. 159–169, December 07 2015.

A. M. García, I. Santé, M. Boullón, and R. Crecente, “Calibration of an urban cellular automaton model by using statistical techniques and a genetic algorithm. Application to a small urban settlement of NW Spain,” Int. J. Geogr. Inf. Sci., vol. 27, no. 8, pp. 1593–1611, November 2013.

Z. Xia, K. Mao, S. Wei, X. Wang, Y. Fang, and S. Yang, “Application of genetic algorithm support vector regression model to predict damping of cantilever beam with particle damper,” J. Low Freq. Noise Vib. Act. Control, vol. 36, no. 2, pp. 138–147, June 2017.

E. Demir and Ö. Akkuş, “An Introductory Study on ‘ How the Genetic Algorithm Works in the Parameter Estimation of Binary Logit Model ?,’” Int. J. Sci. Basic Appl. Res., vol. 19, pp. 162–180, January 2015.

A. Salim and M. R. Alfian, “Optimalisasi Regresi Logistik Menggunakan Algoritma Genetika Pada Data Klasifikasi,” J. Teknol. Inf. dan Terap., vol. 6, no. 2, pp. 50–55, December 2019.

A. Yalçınkaya, İ. G. Balay, and B. Şenoǧlu, “A new approach using the genetic algorithm for parameter estimation in multiple linear regression with long-tailed symmetric distributed error terms: An application to the Covid-19 data,” Chemom. Intell. Lab. Syst., vol. 216, June 2021.

Badan Pusat Statistik, “Laporan Eksekutif Ketenagakerjaan Kota Malang 2020,” BPS Kota Malang, 2020.

Z. Qiu, H. Li, H. Su, G. Ou, and T. Wang, “Logistic regression bias correction for large scale data with rare events,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8347 LNAI, no. PART 2, pp. 133–144, December 2013.

D. W. Hosmer and S. Lemeshow, Applied Logistic Regression Second Edition. Canada: John Wiley and Sons,Inc, 2000.

M. Maalouf and M. Siddiqi, “Weighted logistic regression for large-scale imbalanced and rare events data,” IIE Annu. Conf. Expo 2014, vol. 59, pp. 142–148, March 2014.

E. E. Elmahdy and A. W. Aboutahoun, “A new approach for parameter estimation of finite Weibull mixture distributions for reliability modeling,” Appl. Math. Model., vol. 37, no. 4, pp. 1800–1810, February 2013.

T. Alam et al., “Genetic Algorithm : Reviews , Implementations and Applications,” Int. J. Eng. Pedagog., vol. 10, no. 6, pp. 57–77, August 2020.

S. Katoch, S. S. Chauhan, and V. Kumar, A review on genetic algorithm: past, present, and future, vol. 80, no. 5. Multimedia Tools and Applications, February 2021.

Published
2023-04-20
How to Cite
[1]
D. Menufandu, R. Fitriani, and E. Sumarminingsih, “ESTIMATION OF MAXIMUM LIKELIHOOD WEIGHTED LOGISTIC REGRESSION USING GENETIC ALGORITHM (CASE STUDY: INDIVIDUAL WORK STATUS IN MALANG CITY)”, BAREKENG: J. Math. & App., vol. 17, no. 1, pp. 0487-0494, Apr. 2023.
Issue
Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Dahlia Gladiola Rurina Menufandu, Rahma Fitriani, Eni Sumarminingsih

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