A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
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Artificial Intelligence
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We propose the first paradigm that brings Feature Subset Selection to the realm of ILP, in a setting where examples are expressed as non-recursive Datalog Horn clauses. The main idea is to approximate the original relational problem by a multi-instance attribute-value problem, and to perform Feature Subset Selection on that modified representation, suitable for the task. The method acts as a filter: it preprocesses the relational data, prior to model building, and produces relational examples with empirically irrelevant literals removed. An implementation of the paradigm is proposed and successfully applied to the biochemical mutagenesis domain.