A new approach for interpreting Random Forest models and its application to the biology of ageing
- Submitting institution
-
The University of Kent
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11801
- Type
- D - Journal article
- DOI
-
10.1093/bioinformatics/bty087
- Title of journal
- Bioinformatics
- Article number
- -
- First page
- 2449
- Volume
- 34
- Issue
- 14
- ISSN
- 1367-4803
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2018
- URL
-
https://kar.kent.ac.uk/66666/
- 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
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 19
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a new way to compute the importance of features in data mining algorithms. This is significant in two ways. Firstly, it allows better algorithms that outperform the state of the art in bioinformatics problems with a wide range of applicability in medicine. Secondly, it makes the results of the algorithms interpretable, such that it is possible to reason about the outcomes of the algorithm.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -