A method for multi-relational classification using single and multi-feature aggregation functions
作者： Richard Frank , Flavia Moser , Martin Ester
DOI: 10.1007/978-3-540-74976-9_43
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摘要: This paper presents a novel method for multi-relational classification via an aggregation-based Inductive Logic Programming (ILP) approach. We extend the classical ILP representation by aggregation of multiple-features which aid process allowing analysis relationships and dependencies between different features. In order to efficiently learn rules this rich format, we present algorithm capable performing with use virtual joins data. By using more expressive predicates than existential quantifier used in standard methods, improve accuracy classification. claim is supported experimental evaluation on three real world datasets.
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