Comparison of Imbalanced Data Methods on Logistics Regression (Case Study: Poverty in Indonesia In 2018)

Pardomuan Robinson Sihombing

doi:10.22441/ihasj.2021.v4i3.05

Comparison of Imbalanced Data Methods on Logistics Regression (Case Study: Poverty in Indonesia In 2018)

Pardomuan Robinson Sihombing

Abstract

Poverty is still one of the main problems in economic development and inequality, unemployment, and economic growth. This study aims to model poverty directly by using a discrete choice model using binomial regression. The data used is imbalanced data, where one of the value categories is relatively small. In this study, the logistic regression method applies several resample techniques. They include undersampling, oversampling, a combination of both, and Cost-Sensitive Learning (CSL). The results obtained that both sampling techniques provide optimal results when viewed from the indicators of accuracy, specificity, sensitivity, and AUC. In addition, the results show that in households in rural areas, the head of the household is female, unmarried, has low education, married at an early/old age, and has a large household size, has a greater chance of being poor than other categories. So that targeted and comprehensive policy is needed so that the poverty rate can continue to be reduced and welfare increases

Full Text:

PDF

References

Adhi, E. T. (2009). Pelayanan Sanitasi Buruk Akar Dari Kemiskinan. Jurnal Analisis Sosial, 14, 76–88.

Anyanwu, J. C. (2014). Marital Status, Household Size and Poverty in Nigeria: Evidence from the 2009/2010 Survey Data. African Development Review. 26(1), 118–137. doi:https://doi.org/10.1111/1467-8268.12069

Awan, M., Sarwar, M., W Muhammad, M., & M.Waqas. (2011). Munich Personal RePEc Archive Impact of education on poverty reduction Impact Of Education On Poverty Reduction. International Journal of Academic Research, 3.

Buvinić, M., & Gupta, G. R. (1997). Female-headed households and femalemaintained families: Are they worth targeting to reduce poverty in Developing Countries. Economic Development and Cultural Change, 45(2), 258–280. doi:https://doi.org/10.1086/452273

Czado, C., & Santner, T. (1992). The effect of link misspecification on binary regression inference. J. Statist. Plann. Inference 33, 213–231. MR1190622.

Fahar, F. (2015). Kemiskinan dan Ketenagakerjaan di Kepulauan Riau 2014: Permasalahan Dan Implikasi Kebijakan. Jurnal Ekonomi Keuangan.

Fawcett, T. (2006). An Introduction to ROC Analysis. Journal of Pattern Recognition Letters. An Introduction to ROC Analysis. Journal of Pattern Recognition Letters, 27, 861-874.

Fissuh, E., & Harris, M. (2005). Modeling Determinants of Poverty in Eritrea: A New Approach, 1-35.

Gorunescu, F. (2011). Data Mining Concept, Models and Techniques. Verlag Berlin Heidelberg: Springer.

Gounder, R., & Xing, Z. (2012). Impact of education and health on poverty reduction: Monetary and non-monetary evidence from Fiji. Economic Modeling, 29(3), 787–794. doi:https://doi.org/10.1016/j.econmod.2012.01.018

Han, Jiawei, Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques 3rd Edition. Massachusetts: Elsevier Inc.

King, G., & Zeng, L. (2001). Logistic Regression in Rare Events Data. Journal of Political Analysis, 9(2), 137-163.

Maalouf, M., & Trafalis, T. (2011). Rare Events and Imbalanced Datasets: An Overview. Int. Journal Data Mining, Modelling and Management, 3(4), 375-385.

Macinnes, T., Tinson, A., Hughes, C., Born, T. B., & Aldridge, H. (2015). Monitoring poverty and social exclusion.

Malgesini, G., Cesarini-Sforza, L., Babović, M., Leemkuil, S., Sverrisdóttir, M., & Mareková, S. (2001). Gender and Poverty in Europe. Development, (Ic). 1-14.

Nisbet, Robert, Elder, J., & Miner, G. ( 2009). Handbook of Statistical Analysis and Data. California: Elsevier Inc.

OECD. (2015). Pensions at a Glance 2015: OECD and G20 indicators. Retrieved from https://doi.org/10.1787/pension_glance-2015-en

Rahayu, A. S. (2017). Kehidupan Sosial Ekonomi Single Mother dalam Ranah Domestik dan Publik. Jurnal Analisa Sosiologi, 6(1).

Sigle, W. R., & McLanahan, S. (2002). For richer or poorer? Marriage as an anti-poverty strategy in the United States. Population. 57(3), 509–526. doi:https://doi.org/10.2307/3246637

Sinaga, U., & Siregar, H. (2002). Analisis Determinan Kemiskinan Sebelum dan Sesudah Desentralisasi Fiskal. Jurnal Sosio Economic of Agriculturure and Agribisnis, 6(2), 1–17.

Sridhar, K. S. (2015). Is urban poverty more challenging than rural poverty? A review. Environment and Urbanization Asia, 6(2), 95–108.

Tilak, J. B. (1999). Education and Poverty in South Asia. Prospect, XXIX(4).

UNDP. (1997). In Human Development Report. doi:https://doi.org/10.2307/2524904

DOI: http://dx.doi.org/10.22441/ihasj.2021.v4i3.05