Optimizing Separation Thresholds through Batch Labeling: A Novel Algorithm for Class Imbalance

In this survey, we explore active learning techniques for imbalanced data, where one class dominates the other classes. The majority class is like a tall building, while the minority classes are like small houses scattered around it. Active learning helps to find the needles (minority classes) in the haystack (majority class).
We discuss several active learning methods that can handle imbalanced data, including Batch Active Learning (BAL), Active Sampling (AS), and Query-by-Committee (QBC). BAL is like a teacher who selects students from a balanced dataset to learn from. AS is like a fair lottery system where each student has an equal chance of being selected. QBC is like a committee of students who work together to select the most informative examples for learning.
We also examine the importance of properly tuning the separation threshold, which is like setting the right height for the teacher’s desk. A too-high or too-low desk can lead to inefficient teaching. Similarly, a poorly chosen separation threshold can result in wasted effort on minority classes.
The survey highlights the challenges of evaluating active learning methods for imbalanced data, as traditional metrics like accuracy may not accurately reflect the performance of the model. Instead, we need to use more robust evaluation metrics that take into account the class balance. This is like using a map to navigate uncharted territory; we need to be aware of the terrain’s features and obstacles to find our way successfully.
Finally, we discuss the potential applications of active learning in real-world scenarios, such as image classification, natural language processing, and bioinformatics. Active learning can help to overcome the challenges posed by imbalanced data in these domains, leading to improved model performance and more accurate predictions.
In summary, this survey provides a comprehensive overview of active learning techniques for imbalanced data, highlighting the unique challenges and opportunities presented by this scenario. By using everyday language and engaging analogies, we demystify complex concepts and make the article accessible to a wide range of readers.