International Journal of Data Science and Analysis
Volume 5, Issue 6, December 2019, Pages: 123-127
Received: Sep. 25, 2019;
Accepted: Nov. 8, 2019;
Published: Nov. 17, 2019
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Samuel Adewale Aderoju, Department of Statistics and Mathematical Sciences, Kwara State University, Ilorin, Nigeria
Emmanuel Teju Jolayemi, Department of Statistics, University of Ilorin, Ilorin, Nigeria
Handling classification issues of class imbalance data has gained attentions of researchers in the last few years. Class imbalance problem evolves when one of two classes has more sample than the other class. The class with more sample is called major class while the other one is referred to as minor class. The most classification or predicting models are more focusing on classifying or predicting the major class correctly, ignoring the minor class. In this paper, various data pre-processing approaches to improve accuracy of the models were reviewed with application to terminated pregnancy data. The data were extracted from the 2013 Nigeria Demographic and Health Survey (NDHS). The response variable is “terminated pregnancy” (asking women of reproductive age whether they have ever experienced terminated pregnancy or not), which has two possible classes (“YES” or “NO”) that exhibited class imbalanced. The major class (“NO”) is 86.82% (of the sample) representing Nigerian women of age 15 – 49 years who had never experience terminated pregnancy while the other category (minor class) is 13.18%. Hence, different resampling techniques were exploited to handle the problem and to improve the model performance. Synthetic Minority Oversampling Technique (SMOTE) improved the model best among the resampling techniques considered. The following socio-demographic factors: age, age at first birth, residential area, region, education level of women were significantly associated with having terminated pregnancy in Nigeria.
Samuel Adewale Aderoju,
Emmanuel Teju Jolayemi,
Issues of Class Imbalance in Classification of Binary Data: A Review, International Journal of Data Science and Analysis.
Vol. 5, No. 6,
2019, pp. 123-127.
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