Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression

This paper examines various methods of computing uncertainty and diversity for active learning in genetic programming. We found that the model population in genetic programming can be exploited to select informative training data points by using a model ensemble combined with an uncertainty metric....

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Veröffentlicht in:	IEEE transactions on evolutionary computation 2024-09, p.1-1
Hauptverfasser:	Haut, Nathan, Banzhaf, Wolfgang, Punch, Bill
Format:	Artikel
Sprache:	eng
Schlagworte:	Active learning Computational modeling Data models Genetic programming Labeling Machine learning Mathematical models Measurement symbolic regression Training Training data Uncertainty
Online-Zugang:	Volltext
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