Handling Imbalanced Data: A Case Study for Binary Class Problems
This work addresses the issue of misleading model results due to imbalanced data for researchers and practitioners in machine learning, but it is incremental as it focuses on explaining existing techniques rather than introducing new methods.
The paper tackles the problem of imbalanced data in binary classification by explaining and manually computing synthetic data points for SMOTE and ADASYN techniques, analyzing their application across various imbalance ratios and sample sizes.
For several years till date, the major issues in terms of solving for classification problems are the issues of Imbalanced data. Because majority of the machine learning algorithms by default assumes all data are balanced, the algorithms do not take into consideration the distribution of the data sample class. The results tend to be unsatisfactory and skewed towards the majority sample class distribution. This implies that the consequences as a result of using a model built using an Imbalanced data without handling for the Imbalance in the data could be misleading both in practice and theory. Most researchers have focused on the application of Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) Sampling Approach in handling data Imbalance independently in their works and have failed to better explain the algorithms behind these techniques with computed examples. This paper focuses on both synthetic oversampling techniques and manually computes synthetic data points to enhance easy comprehension of the algorithms. We analyze the application of these synthetic oversampling techniques on binary classification problems with different Imbalanced ratios and sample sizes.