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This paper investigates an important problem in stream mining, i.e.,classification under streaming emerging new classes or SENC. The commonapproach is to treat it as a classification problem and solve it using either asupervised learner or a semi-supervised learner. We propose an alternativeapproach by using unsupervised learning as the basis to solve this problem. TheSENC problem can be decomposed into three sub problems: detecting emerging newclasses, classifying for known classes, and updating models to enableclassification of instances of the new class and detection of more emerging newclasses. The proposed method employs completely random trees which have beenshown to work well in unsupervised learning and supervised learningindependently in the literature. This is the first time, as far as we know,that completely random trees are used as a single common core to solve allthree sub problems: unsupervised learning, supervised learning and model updatein data streams. We show that the proposed unsupervised-learning-focused methodoften achieves significantly better outcomes than existingclassification-focused methods.

Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees