- Title
- New class adaptation via instance generation in one-pass class incremental learning
- Creator
- Zhu, Yue; Ting, Kaiming; Zhou, Zhi-Hua
- Date
- 2017
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/163461
- Identifier
- vital:12862
- Identifier
-
https://doi.org/10.1109/ICDM.2017.163
- Abstract
- One pass learning updates a model with only a single scan of the dataset, without storing historical data. Previous studies focus on classification tasks with a fixed class set, and will perform poorly in an open dynamic environment when new classes emerge in a data stream. The performance degrades because the classifier needs to receive a sufficient number of instances from new classes to establish a good model. This can take a long period of time. In order to reduce this period to deal with any-time prediction task, we introduce a framework to handle emerging new classes called One-Pass Class Incremental Learning (OPCIL). The central issue in OPCIL is: how to effectively adapt a classifier of existing classes to incorporate emerging new classes. We call it the new class adaptation issue, and propose a new approach to address it, which requires only one new class instance. The key is to generate pseudo instances which are optimized to satisfy properties that produce a good discriminative classifier. We provide the necessary propertiesand optimization procedures required to address this issue. Experiments validate the effectiveness of this approach.
- Relation
- 2017 IEEE International Conference on Data Mining (ICDM)
- Rights
- © 2017 IEEE
- Rights
- This metadata is freely available under a CCO license
- Reviewed
- Hits: 638
- Visitors: 605
- Downloads: 0
| Thumbnail | File | Description | Size | Format |
|---|