A Comparison of Approaches for Learning First-Order Logical Probability Estimation Trees

@inproceedings{Fierens2005ACO,
  title={A Comparison of Approaches for Learning First-Order Logical Probability Estimation Trees},
  author={Daan Fierens and Jan Ramon and Hendrik Blockeel and Maurice Bruynooghe},
  year={2005}
}
Probability Estimation Trees (PETs) [9] try to estimate the probability with which an instance belongs to a certain class, rather than just predicting its most likely class. Several approaches for learning PETs have been proposed, mainly in a propositional context. Since we are interested in applying PETs in a relational context, we make some simple modifications to the first-order tree learner Tilde to incorporate the main approaches (and a novel variant) and we experiment with all of them… CONTINUE READING

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