Online Active Learning of Reject Option Classifiers

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

Classification with Reject Option in Text Categorisation Systems

The aim of this paper is to evaluate the potential usefulness of the reject option for text categorisation (TC) tasks. The reject option is a technique used in statistical pattern recognition for improving classification reliability. Our work is motivated by the fact that, although the reject option proved to be useful in several pattern recognition problems, it has not yet been considered for TC tasks. Since TC tasks differ from usual pattern recognition problems in the performance measures used and in the fact that documents can belong to more than one category, we developed a specific rejection technique for TC problems. The performance improvement achievable by using the reject option was experimentally evaluated on the Reuters dataset, which is a standard benchmark for TC systems