A Comparison of Approaches for Learning First-Order Logical Probability Estimation Trees
نویسندگان
چکیده
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. Our results provide insight into the strengths and weaknesses of the alternative approaches in a relational context.
منابع مشابه
A Comparison of Approaches for Learning Probability Trees
Probability trees (or Probability Estimation Trees, PET’s) are decision trees with probability distributions in the leaves. Several alternative approaches for learning probability trees have been proposed but no thorough comparison of these approaches exists. In this paper we experimentally compare the main approaches using the relational decision tree learner Tilde (both on non-relational and ...
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