An Oversampling Technique with Descriptive Statistics

Abstract

Oversampling is often applied as a means to win a better knowledge model. Several oversampling methods based on synthetic instances have been suggested, and SMOTE is one of the representative oversampling methods that can generate synthetic instances of a minor class. Until now, the oversampled data has been used conventionally to train machine learning models without statistical analysis, so it is not certain that the machine learning models will be fine for unseen cases in the future. However, because such synthetic data is different from the original data, we may wonder how much it resembles the original data so that the oversampled data is worth using to train machine learning models. For this purpose, I conducted this study on a representative dataset called wine data in the UCI machine learning repository, which is one of the datasets that has been experimented with by many researchers in research for knowledge discovery models. I generated synthetic data iteratively using SMOTE, and I compared the synthetic data with the original data of wine to see if it was statistically reliable using a box plot and t-test. Moreover, since training a machine learning model by supplying more high-quality training instances increases the probability of obtaining a machine learning model with higher accuracy, it was also checked whether a better machine learning model of random forests can be obtained by generating much more synthetic data than the original data and using it for training the random forests. The results of the experiment showed that small-scale oversampling produced synthetic data with statistical characteristics that were statistically slightly different from the original data, but when the oversampling rate was relatively high, it was possible to generate data with statistical characteristics similar to the original data, in other words, after generating high-quality training data, and by using it to train the random forests, it was possible to generate random forests with higher accuracy than using the original data alone, from 97.75% to 100%. Therefore, by supplying additional statistically reliable synthetic data as a way of oversampling, it was possible to create a machine-learning model with a higher predictive rate.