Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33486-3_56 | ECML/PKDD |
Keywords | Field | DocType |
specific aspect,large set,knowledge discovery,genetic programming,relevant variable,open-source software,model simplification,combinatorial optimization,symbolic regression,rich gui | Regression,Computer science,Genetic programming,Combinatorial optimization,Software,Knowledge extraction,Artificial intelligence,Symbolic regression,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.35 | 2 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Kronberger | 1 | 192 | 25.40 |
| Stefan Wagner | 2 | 1 | 0.35 |
| Michael Kommenda | 3 | 97 | 15.58 |
| Andreas Beham | 4 | 77 | 20.20 |
| Andreas Scheibenpflug | 5 | 7 | 2.54 |
| Michael Affenzeller | 6 | 339 | 62.47 |