Existing research on multiclass text classification mostly makes the closed world assumption, which focuses on designing accurate classifiers under the assumption that all test classes are known at training time. A more realistic scenario is to expect unseen classes during testing (open world). In this case, the goal is to design a learning system that classifies documents of the known classes into their respective classes and also to reject documents from unknown classes. This problem is called open (world) classification. This paper approaches the problem by reducing the open space risk while balancing the empirical risk. It proposes to use a new learning strategy, called center-based similarity (CBS) space learning (or CBS learning), to provide a novel solution to the problem. Extensive experiments across two datasets show that CBS learning gives promising results on multiclass open text classification compared to state-of-the-art baselines.
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