Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data
- Submitting institution
-
University of East London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 30
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2019.2899578
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 24649
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
-
-
- 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
-
3
- Research group(s)
-
1 - Intelligent Systems
- Citation count
- 16
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Most real-life datasets are imbalanced because the classes within the dataset are not evenly distributed. This research investigated how to obtain an accurate prediction from an imbalanced dataset, which can significantly impact the prediction from healthcare datasets. The proposed approach showed more accurate results than the research benchmarks. It provided an enhanced accuracy in grading and measuring the similarities within datasets. This paper led to the establishment of an international collaboration and won a deanship grant from King Saud University, which gave us the opportunity to publish another article [1].
[1] https://doi.org/10.3390/sym11121504
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -