Recognizing and Learning Unknown Emerging Concepts

Abstract

We study the classification of data in which some of the concepts represented by the data are known in advance, while new, emerging, concepts are discovered as they appear in the data. The resulting paradigm is called Concept Emergence. Unlike clustering, we start with some known classes, but then learn emerging concepts using new unlabeled data. Unlike Concept Drift, we assume the original concepts remain stationary. This differs from outlier detection because we use the rejected samples to update the classifier. We illustrate the method on both synthetic and real data sets using Support Vector Machine classifiers.