2016 5th Brazilian Conference on Intelligent Systems (BRACIS) /

Sequential Symbolic Regression (SSR) is a technique that recursively induces functions over the error of the current solution, concatenating them in an attempt to reduce the error of the resulting model. As proof of concept, the method was previously evaluated in one-dimensional problems and compare...

Ausführliche Beschreibung

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Weitere Titel:
2016 5th Brazilian Conference on Intelligent Systems
Körperschaft:
Institute of Electrical and Electronics Engineers
Format:
Elektronisch E-Book
Sprache:
Englisch
Veröffentlicht:
Piscataway, New Jersey : IEEE, 2016.
Zusammenfassung:
Sequential Symbolic Regression (SSR) is a technique that recursively induces functions over the error of the current solution, concatenating them in an attempt to reduce the error of the resulting model. As proof of concept, the method was previously evaluated in one-dimensional problems and compared with canonical Genetic Programming (GP) and Geometric Semantic Genetic Programming (GSGP). In this paper we revisit SSR exploring the method behaviour in higher dimensional, larger and more heterogeneous datasets. We discuss the difficulties arising from the application of the method to more complex problems, e.g., overfitting, along with suggestions to overcome them. An experimental analysis was conducted comparing SSR to GP and GSGP, showing SSR solutions are smaller than those generated by the GSGP with similar performance and more accurate than those generated by the canonical GP.
Umfang:
1 online resource
ISBN:
9781509035663
1509035664
Schlagworte:
Artificial intelligence > Congresses.
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