Handling Imbalanced Data: A Case Study for Binary Class Problems

TL;DR

This paper examines the challenges of imbalanced data in binary classification, compares synthetic oversampling techniques like SMOTE and ADASYN, and provides detailed algorithm explanations with computed examples.

Contribution

It offers a detailed explanation of SMOTE and ADASYN algorithms with computed examples, enhancing understanding of handling imbalanced data.

Findings

Synthetic oversampling improves classification balance

SMOTE and ADASYN effectiveness varies with imbalance ratio

Manual computation clarifies algorithm processes

Abstract

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.