Overlap Aware Active Learning Query Strategies for Pool Based Scenario

Abstract

Active learning (AL) is an emerging machine learning technique, which works with fewer labeled instances, thereby reducing labeling as well as learning cost. Various AL query strategies are available to identify the candidate query instances for achieving a generalized hypothesis. However, the AL query strategies sometimes produce redundant or overlapped instances as candidate instances, due to the presence of instance and/or class overlapping in the dataset. This overlapping results in a complex decision boundary. We addressed this problem and proposed a solution as an augmented step for AL query strategies, which improves the generalization performance of the AL task. The solution is based on the nearest neighbor assumption and fixed vicinity based approach. This augmentation tries to avoid the selection of overlap instances and provides a smoother decision boundary. We have validated the performance of the proposed method on the available benchmark datasets.