Open-set pattern recognition and its application in information extraction from text - Ke, Yizhen (Nanyang Technological University, 2023)
In traditional supervised learning, the training set contains the same classes that appear in the testing set. However, the classifier may encounter previously unseen classes in the actual world, which is likely to create errors if a close-set classifier divides these data into the original category. The open-set classifier is designed to classify known samples accurately and reject unrelated samples. However, there are fewer applications in text classification. The goal of this paper is to achieve the application of open-set recognition on text classification tasks.