Meta methods for model sharing in personal information systems
ACM Transactions on Information Systems (TOIS)
Classification with a Reject Option using a Hinge Loss
The Journal of Machine Learning Research
Adaptive classification with jumping emerging patterns
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Transactions on rough sets XII
A classification approach with a reject option for multi-label problems
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Ensemble of binary learners for reliable text categorization with a reject option
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Multi-label classification with a reject option
Pattern Recognition
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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.