Class-imbalance is very common in real world. However, traditional state-of-the-art classifiers do not work well on imbalanced data sets for imbalanced class distribution. This paper considers imbalance learning from the viewpoint of ensemble pruning

Background: The concept of self-paced learning in the context of ensemble learning involves the idea of allowing each individual member, or base learner, within an ensemble to learn at its own pace. Ensemble learning refers to a machine learning technique that combines multiple learning models, known as base learners, to improve predictive accuracy and overall performance. Motivation: The research focuses on self-paced ensemble and big data classifications, with considerable data imbalance as a mediating factor. This idea is a brand-new domain with a lot of untapped potential. For example, the growth of information technology has resulted in the spread of massive data in our daily lives. Many real-world applications often create imbalanced datasets for critical classification tasks. For example, to anticipate click-through rates, online advertising companies may produce many datasets, such as user viewing or interactions with advertisements Research object : This research focuses on the challenges associated with learning classifiers from large-scale, highly imbalanced datasets prevalent in many real-world applications. Traditional algorithms learning often need better performance and high computational efficiency when dealing with imbalanced data. Factors such as class imbalance, noise, and class overlap make it demanding to learn effective classifiers. Methods: The self-paced ensemble method addresses the challenges of high imbalance ratios, class overlap, and noise presence in large-scale imbalanced classification problems. By incorporating the knowledge of these challenges into our learning framework, we establish the concept of classification hardness distribution Conclusion: This research concludes that the self-paced ensemble is a revolutionary learning paradigm for massive imbalance categorization, capable of improving the performance of existing learning algorithms on imbalanced data and providing better results for future applications.