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Block-wise construction of tree-like relational features with monotone reducibility and redundancy

Block-wise construction of tree-like relational features with monotone reducibility and redundancy,10.1007/s10994-010-5208-5,Machine Learning,Ondřej K

Block-wise construction of tree-like relational features with monotone reducibility and redundancy   (Citations: 1)
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We describe an algorithm for constructing a set of tree-like conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional level-wise approaches which preserve the monotonicity of frequency, our block-wise approach preserves monotonicity of feature reducibility and redundancy, which are important in propositionalization employed in the context of classification learning. With pruning based on these properties, our block-wise approach efficiently scales to features including tens of first-order atoms, far beyond the reach of state-of-the art propositionalization or inductive logic programming systems.
Journal: Machine Learning - ML , vol. 83, no. 2, pp. 163-192, 2011
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    • ...The feature construction algorithm for tree-like features is based on the feature-construction algorithm from [16]...
    • ...Algorithms for computing domains and term-domains have been described in [16] and they also correspond to well-known algorithms for answering acyclic conjunctive queries [22]...

    Ondřej Kuželkaet al. Gaussian Logic for Predictive Classification

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