A Non-Canonical Hybrid Metaheuristic Approach to Adaptive Data Stream Classification
- Submitting institution
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Birmingham City University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11Z_OP_D0045
- Type
- D - Journal article
- DOI
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10.1016/j.future.2019.07.067
- Title of journal
- Future Generation Computer Systems
- Article number
- -
- First page
- 127
- Volume
- 102
- Issue
- -
- ISSN
- 0167-739X
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
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https://www.sciencedirect.com/science/article/pii/S0167739X18332497
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In addition to advancing classical metaheuristic methods (as acknowledged by https://doi.org/10.1140/epjp/s13360-020-00440-6), the study combines the proposed algorithms in a novel ensemble technique allowing to improve classification accuracy over evolving data streams compared to the state-of-the-art algorithms (as acknowledged by https://doi.org/10.1109/ACCESS.2020.2998482). This is a significant contribution providing that the majority of modern applications such as the Internet of Things, social media and financial instruments are powered by data coming as streams in real time. The method informed the development of other classification and clustering algorithms (e.g. https://doi.org/10.1016/j.future.2020.08.031) and was used as a benchmark in another study (https://doi.org/10.1109/ACCESS.2019.2954993).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -