Back to Search
Start Over
Symbolic-Regression Boosting
- Publication Year :
- 2022
- Publisher :
- arXiv, 2022.
-
Abstract
- Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages -- between 2--5 -- to a symbolic regressor, statistically significant improvements can often be attained. We note that coding SyRBo on top of any symbolic regressor is straightforward, and the added cost is simply a few more evolutionary rounds. SyRBo is essentially a simple add-on that can be readily added to an extant symbolic regressor, often with beneficial results.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Boosting (machine learning)
Computer science
0102 computer and information sciences
02 engineering and technology
Machine learning
computer.software_genre
01 natural sciences
Theoretical Computer Science
Machine Learning (cs.LG)
Extant taxon
Simple (abstract algebra)
0202 electrical engineering, electronic engineering, information engineering
Neural and Evolutionary Computing (cs.NE)
business.industry
Small number
Computer Science - Neural and Evolutionary Computing
Regression
Computer Science Applications
010201 computation theory & mathematics
Hardware and Architecture
020201 artificial intelligence & image processing
Gradient boosting
Artificial intelligence
business
Symbolic regression
computer
Software
Coding (social sciences)
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....150ad73473ca2f600fa0f70f11269b9b
- Full Text :
- https://doi.org/10.48550/arxiv.2206.12082