Exploring Strategies and Algorithms for Tackling Class Imbalance Across Varied Data Types

In the current internet era, data is widely dispersed across several domains. However, a large proportion of these datasets have unbalanced distributions, where some groups are obviously more common than others. This uneven distribution of data across multiple classes is one of the main problem for unbalanced learning, which makes it harder for classifiers to learn from classes present in smaller numbers. There are number of oversampling and undersampling techniques available to avoid this problem. Ensemble approaches like ADASYN, RUSBoost, and combining techniques like SMOTETomek are popular ways to deal with unbalanced data. We compared ADASYN, RUSBoost, and SMOTETomek on three real-world datasets. This paper explores the pros and cons of sampling techniques used to tackle imbalanced data in machine learning. We highlight key benefits such as improved model performance and reduced training time, while also addressing potential challenges like information loss and overfitting. We offer recommendations for choosing suitable sampling methods based on class distribution and model requirements. Through experiments with hybrid approaches at different levels of imbalance, we demonstrate that these strategies enhance the robustness of models, leading to better overall