Development of a Physical Impairment Prediction Model for Korean Elderly People using Synthetic Minority Over-Sampling Technique and XGBoost

SMOTE: Synthetic Minority Over-sampling Technique

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of “normal” examples with only a small percentage of “abnormal” or “interesting” examples. It is also the case that the cost of misclassifying an abnormal (i...

ovarian cancer classification using hybrid synthetic minority over-sampling technique and neural network

every woman is at risk of ovarian cancer; about 90 percent of women who develop ovarian cancer are above 40 years of age, with the high number of ovarian cancers occurring at the age of 60 years and above. early and correct diagnosis of ovarian cancer can allow proper treatment and as a result reduce the mortality rate. in this paper, we proposed a hybrid of synthetic minority over-sampling tec...

Improving accuracy of students' final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique

There is a perpetual elevation in demand for higher education in the last decade all over the world; therefore, the need for improving the education system is imminent. Educational data mining is a newly-visible area in the field of data mining and it can be applied to better understanding the educational systems in Bangladesh. In this research, we present how data can be preprocessed using a d...

An Improved Intrusion Detection Approach using Synthetic Minority Over-Sampling Technique and Deep Belief Network

An Analysis of Classification of Imbalanced Datasets by Using Synthetic Minority Over-Sampling Technique

Abstract—Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalance...