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Journal of Intelligent Information Systems

Erschienen in:

01.04.2014

A method for reduction of examples in relational learning

verfasst von: Ondřej Kuželka, Andrea Szabóová, Filip Železný

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 2/2014

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Abstract

Feature selection methods often improve the performance of attribute-value learning. We explore whether also in relational learning, examples in the form of clauses can be reduced in size to speed up learning without affecting the learned hypothesis. To this end, we introduce the notion of safe reduction: a safely reduced example cannot be distinguished from the original example under the given hypothesis language bias. Next, we consider the particular, rather permissive bias of bounded treewidth clauses. We show that under this hypothesis bias, examples of arbitrary treewidth can be reduced efficiently. We evaluate our approach on four data sets with the popular system Aleph and the state-of-the-art relational learner nFOIL. On all four data sets we make learning faster in the case of nFOIL, achieving an order-of-magnitude speed up on one of the data sets, and more accurate in the case of Aleph.

Vorheriger Artikel Semantic subgroup explanations

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In this paper we follow the conventions of Atserias et al. (2007). In other works, what we call k-consistency is known as strong k+1-consistency (Rossi et al. 2006).

This is not always the case. For example, we have a decision procedure for x-subsumption w.r.t. the set of all clauses – the ordinary 𝜃-subsumption which is decidable but NP-hard.

Note again the terminology used in this paper following Atserias et al. (2007). In CSP-literature, it is often common to call 2-consistency what we call 1-consistency.

We are applying 𝜃 also to e because e need not be ground.

A first-order interpretation consists of several components, one them is the domain of discourse, and another is a function ϕ which maps constants to elements of the domain of discourse. We assume w.l.o.g that there is a constant c _d for every element d of the domain of discourse.

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Titel: A method for reduction of examples in relational learning
verfasst von: Ondřej Kuželka
Andrea Szabóová
Filip Železný
Publikationsdatum: 01.04.2014
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 2/2014
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-013-0294-z

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