Knowledge Discovery Through Symbolic Regression with HeuristicLab
Abstract
This contribution describes how symbolic regression can be used for knowledge discovery with the open-source software HeuristicLab. HeuristicLab includes a large set of algorithms and problems for combinatorial optimization and for regression and classification, including symbolic regression with genetic programming. It provides a rich GUI to analyze and compare algorithms and identified models. This contribution mainly focuses on specific aspects of symbolic regression that are unique to HeuristicLab, in particular, the identification of relevant variables and model simplification.
Cite
Text
Kronberger et al. "Knowledge Discovery Through Symbolic Regression with HeuristicLab." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33486-3_56Markdown
[Kronberger et al. "Knowledge Discovery Through Symbolic Regression with HeuristicLab." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/kronberger2012ecmlpkdd-knowledge/) doi:10.1007/978-3-642-33486-3_56BibTeX
@inproceedings{kronberger2012ecmlpkdd-knowledge,
title = {{Knowledge Discovery Through Symbolic Regression with HeuristicLab}},
author = {Kronberger, Gabriel and Wagner, Stefan and Kommenda, Michael and Beham, Andreas and Scheibenpflug, Andreas and Affenzeller, Michael},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {824-827},
doi = {10.1007/978-3-642-33486-3_56},
url = {https://mlanthology.org/ecmlpkdd/2012/kronberger2012ecmlpkdd-knowledge/}
}