Back to Search
Start Over
Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees.
- Source :
- IEEE Transactions on Knowledge & Data Engineering; Aug2017, Vol. 29 Issue 8, p1605-1618, 14p
- Publication Year :
- 2017
-
Abstract
- 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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 29
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 124027476
- Full Text :
- https://doi.org/10.1109/TKDE.2017.2691702