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Inductive Logic Boosting
Recent years have seen a surge of interest in Probabilistic Logic Programming
(PLP) and Statistical Relational Learning (SRL) models that combine logic with
probabilities. Structure learning of these systems is an intersection area of
Inductive Logic Programming (ILP) and statistical learning (SL). However, ILP
cannot deal with probabilities, SL cannot model relational hypothesis. The
biggest challenge of integrating these two machine learning frameworks is how
to estimate the probability of a logic clause only from the observation of
grounded logic atoms. Many current methods models a joint probability by
representing clause as graphical model and literals as vertices in it. This
model is still too complicate and only can be approximate by pseudo-likelihood.
We propose Inductive Logic Boosting framework to transform the relational
dataset into a feature-based dataset, induces logic rules by boosting Problog
Rule Trees and relaxes the independence constraint of pseudo-likelihood.
Experimental evaluation on benchmark datasets demonstrates that the AUC-PR and
AUC-ROC value of ILP learned rules are higher than current state-of-the-art SRL
methods.Comment: 19 pages, 2 figure