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• W124936718 abstract "Text categorization is a key task in information retrieval and natural language processing. Providing a reliability measure of the classification result for a text document into a particular category can benefit the recognition rate as well as better inform the user with regard to the confidence that should be attributed to the output. A novel reliability measure is proposed starting from running different binary classifiers in the Error-Correcting Output Codes (ECOC) framework. Documents classified in a particular category which have a higher ECOC-computed distance from their classification in the next ranked category also have a higher associated reliability. This is the main idea explored in the proposed ECOC-based text classifier with a reject option. Experiments performed for some commonly used text categorization benchmark datasets demonstrate the potential of the proposed method." @default.
• W124936718 created "2016-06-24" @default.
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• W124936718 date "2012-01-01" @default.
• W124936718 modified "2023-09-26" @default.
• W124936718 title "Ensemble of Binary Learners for Reliable Text Categorization with a Reject Option" @default.
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• W124936718 doi "https://doi.org/10.1007/978-3-642-28942-2_13" @default.
• W124936718 hasPublicationYear "2012" @default.
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