# Online Active Learning of Reject Option Classifiers

## TL;DR

This paper introduces new active learning algorithms for reject option classifiers, providing theoretical guarantees and demonstrating their effectiveness in reducing labeled data requirements.

## Contribution

It presents novel active learning algorithms specifically designed for reject option classifiers, including new loss functions and theoretical mistake bounds and convergence guarantees.

## Key findings

- Algorithms effectively reduce labeled data needed
- Theoretical mistake bounds established
- Experimental results confirm efficiency

## Abstract

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.