Confidence Measure for Czech Document Classification

This paper deals with automatic document classification in the context of a real application for the Czech News Agency (CTK). The accuracy our classifier is high, however it is still important to improve the classification results. The main goal of this paper is thus to propose novel confidence measure approaches in order to detect and remove incorrectly classified samples. Two proposed methods are based on the posterior class probability and the third one is a supervised approach which uses another classifier to determine if the result is correct. The methods are evaluated on a Czech newspaper corpus. We experimentally show that it is beneficial to integrate the novel approaches into the document classification task because they significantly improve the classification accuracy.