On Expressiveness and Uncertainty Awareness in Rule-based Classification for Data Streams
- Submitting institution
-
Birmingham City University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11Z_OP_D0027
- Type
- D - Journal article
- DOI
-
10.1016/j.neucom.2017.05.081
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 127
- Volume
- 265
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
-
https://www.sciencedirect.com/science/article/pii/S0925231217310172
- Supplementary information
-
-
- 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
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper proposes a novel technique for data stream classification, namely, Hoeffding Rules. The technique provides an efficient interpretable rule-based model for the streaming classification problem, facilitating trust in the decision making process. Additionally, handling uncertainty through abstaining is a unique feature to this method, as only real-time classification is provided with high certainty. The experimental work proved the superiority of the method in terms of accuracy, when compared with the state-of-the-art rule-based stream classification method, VFDR. Hoeffding Rules inspired more rule-based methods to be developed.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
