An Efficient Many-Class Active Learning Framework for Knowledge-Rich Domains

Abstract

The high cost for labeling data instances is a key bottleneck for training effective supervised learning models. This is especially the case in domains such as medicine and bioinformatics, where expert knowledge is required for understanding and extracting the underlying semantics of data. Active learning provides a means to reduce human labeling efforts by identifying the most informative data instances. In this paper, we propose a cost-effective active learning framework to further lessen human efforts, especially in knowledge-rich domains where a large number of classes may be subject to scrutiny during decision making. In particular, this framework employs a novel many-class sampling model, MC-S, for data sample selection. MC-S is further augmented with convex hull-based sampling to achieve faster convergence of active learning. Evaluation studies conducted over multiple real-world datasets with many classes demonstrate that the proposed framework significantly reduces the overall labeling efforts through fast convergence and early stop of active learning.