Optimal batch selection for active learning in multi-label classification

Conference/Journal
ACM
Authors
Shayok Chakraborty Vineeth N Balasubramanian Sethuraman Panchanathan
BibTex
Abstract
Abstract Multi-label classification is a generalization of conventional classification, where it is possible for a single data point to have multiple labels. Manual annotation of a multi-label data point requires a human oracle to consider the presence/absence of every possible class separately, which involves significant labor. Active learning techniques are effective in reducing human labeling effort to induce a classification model. When exposed to large quantities of unlabeled data, such algorithms automatically select the salient and ...