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Ensemble of Binary Learners for Reliable Text Categorization with a Reject Option

机译：二进制学习者组合，用于带有拒绝选项的可靠文本分类

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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.
机译：文本分类是信息检索和自然语言处理中的关键任务。为文本文档分类到特定类别中提供可靠度度量可以提高识别率，并更好地告知用户应归因于输出的置信度。从在纠错输出代码（ECOC）框架中运行不同的二进制分类器开始，提出了一种新颖的可靠性措施。归类到特定类别的文档比其在下一个排名类别中的类别具有更高的ECOC计算距离，也具有更高的关联可靠性。这是在建议的基于ECOC的带有拒绝选项的文本分类器中探索的主要思想。对一些常用的文本分类基准数据集进行的实验证明了该方法的潜力。

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