Ng, WWY, Zhang, J, Lai, CS orcid.org/0000-0002-4169-4438 et al. (3 more authors) (2019) Cost-Sensitive Weighting and Imbalance-Reversed Bagging for Streaming Imbalanced and Concept Drifting in Electricity Pricing Classification. IEEE Transactions on Industrial Informatics, 15 (3). pp. 1588-1597. ISSN 1551-3203
Abstract
In data streaming environments such as smart grid, it is impossible to restrict arriving data to have the same number of samples in each class. Hence, in addition to concept drift, classification problems in streaming data environments are inherently imbalanced. However, these problems in smart grid have rarely been studied. Incremental learning aims to learn correct classification for future unseen samples from the given streaming data. In this work, we propose a new incremental ensemble learning method to handle both issues. The class imbalance issue is tackled by an imbalance-reversed bagging method while the adaptation to concept drift is achieved by a dynamic cost-sensitive weighting scheme for component classifiers. The proposed methodology is applied to a case study for the electricity pricing in New South Wales and Victoria, Australia. Experimental results show the effectiveness of the proposed algorithm with statistical significance in comparison to the state-of-the-art incremental learning methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Electricity Pricing; Imbalanced Classification; Incremental Learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Jul 2018 12:29 |
Last Modified: | 13 Mar 2019 13:48 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TII.2018.2850930 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133914 |