Data-Driven Model for Optimizing Active Learning

Data-driven models depend on extensive datasets for precise predictions; yet, acquiring adequate labeled data for training these models is a challenge, especially with medical datasets that are constrained by privacy considerations, resulting in a deficiency of labeled data. Active Learning (AL) has developed as a cost-effective strategy that minimizes the quantity of labeled data required for training by selecting the most informative samples. The performance of active learning methods is significantly influenced by data quality characteristics, and due to a lack of direction in selecting the most suitable active learning approach. The study presents a data-driven selection approach that suggests appropriate active learning methods based on dataset characteristics. The study examines the characteristics of the dataset and their impact on active learning performance, revealing significant correlations between data quality issues and the efficacy of active learning approaches. A rule-based selection model is subsequently constructed and verified by experiments and case studies across various datasets. The findings demonstrated consistent alignment between suggested and practically effective techniques. Statistical analysis verifies that the data-driven selection model exhibits reliability exceeding chance agreement, indicating its robustness and practical application in recommending AL techniques selection.