Online Active Learning of Reject Option Classifiers
This work addresses a gap in active learning for reject option classification, which is incremental as it extends existing binary classification methods to a more complex setting.
The paper tackles the problem of active learning for reject option classifiers, which was previously unaddressed, by proposing novel algorithms using double ramp and double sigmoid loss functions, with results showing efficient reduction in the number of labeled examples required.
Active learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in machine learning. However, active learning of the reject option classifier remains unaddressed. In this paper, we propose novel algorithms for active learning of reject option classifiers. We develop an active learning algorithm using double ramp loss function. We provide mistake bounds for this algorithm. We also propose a new loss function called double sigmoid loss function for reject option and corresponding active learning algorithm. We offer a convergence guarantee for this algorithm. We provide extensive experimental results to show the effectiveness of the proposed algorithms. The proposed algorithms efficiently reduce the number of label examples required.