Feature Construction Using Theory-Guided Sampling and Randomised Search
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
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Devising algorithms for learning from multi-relational data is currently considered an important challenge. The wealth of traditional single-relational machine learning tools, on the other hand, calls for methods of 'propositionalization', ie. conversion of multi-relational data into single-relational representations. A major stream of propositionalization algorithms is based on the construction of truth-valued features (first-order logic atom conjunctions), which capture relational properties of data and play the role of binary attributes in the resulting single-table representation. Such algorithms typically use backtrack depth first search for the syntactic construction of features complying to user's mode/type declarations. As such they incur a complexity factor exponential in the maximum allowed feature size. Here we present a polynomial-runtime alternative based on an efficient reduction between the feature construction problem on the propositional satisfiability (SAT) problem, such that the latter involves only Horn clauses and is therefore efficiently solvable.