Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets
- Submitting institution
-
The University of Kent
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9557
- Type
- D - Journal article
- DOI
-
10.1109/TEVC.2013.2291813
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 873
- Volume
- 18
- Issue
- 6
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2014
- URL
-
https://kar.kent.ac.uk/45928/
- 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
-
3
- Research group(s)
-
-
- Citation count
- 28
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work proposes a transformative approach that automatically designs decision tree algorithms. This paper is significant for the future design of decision tree induction algorithms as previously only manually designed algorithms existed. Automating the process of designing algorithms allows much greater flexibility to find, in a data-driven way, the best algorithm and its best configuration (hyper-parameter settings) for each input dataset.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
