Details

Autor(en) / Beteiligte

• Mu, Xin
• Ting, Kai Ming
• Zhou, Zhi-Hua

Titel

Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees

Ist Teil von

• IEEE transactions on knowledge and data engineering, 2017-08, Vol.29 (8), p.1605-1618

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2017

Quelle

IEEE Xplore

Beschreibungen/Notizen

• This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The SENC problem can be decomposed into three subproblems: detecting emerging new classes, classifying known classes, and updating models to integrate each new class as part of known classes. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi-supervised learner. We propose an alternative approach by using unsupervised learning as the basis to solve this problem. The proposed method employs completely-random trees which have been shown to work well in unsupervised learning and supervised learning independently in the literature. The completely-random trees are used as a single common core to solve all three subproblems: unsupervised learning, supervised learning, and model update on data streams. We show that the proposed unsupervised-learning-focused method often achieves significantly better outcomes than existing classification-focused methods.