Galaxy-X: A Novel Approach for Multi-class Classification in an Open Universe
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Classification is a fundamental task in machine learning and artificial intelligence. Existing classification methods are designed to classify unknown instances within a set of previously known classes that are seen in training. Such classification takes the form of prediction within a closed-set. However, a more realistic scenario that fits the ground truth of real world applications is to consider the possibility of encountering instances that do not belong to any of the classes that are seen in training, i.e., an open-set classification. In such situation, existing closed-set classification methods will assign a training label to these instances resulting in a misclassification. In this paper, we introduce Galaxy-X, a novel multi-class classification method for open-set problem. For each class of the training set, Galaxy-X creates a minimum bounding hyper-sphere that encompasses the distribution of the class by enclosing all of its instances. In such manner, our method is able to distinguish instances resembling previously seen classes from those that are of unseen classes. To adequately evaluate open-set classification, we introduce a novel evaluation procedure. Experimental results on benchmark datasets as well as on synthetic datasets show the efficiency of our approach in classifying novel instances from known as well as unknown classes.
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