Show simple item record King, Ross Donald Srinivasan, A. Bain, M. E. 2006-04-25T15:32:45Z 2006-04-25T15:32:45Z 2003-07
dc.identifier.citation King , R D , Srinivasan , A & Bain , M E 2003 , ' An Empirical Study of the Use of Relevance Information in Inductive Logic Programming ' Journal of Machine Learning Research , vol 4 , pp. 369-383 . en
dc.identifier.issn 1532-4435
dc.identifier.other PURE: 68380
dc.identifier.other PURE UUID: 9100c7bd-221b-4966-9a52-81e5976180aa
dc.identifier.other dspace: 2160/149
dc.identifier.uri en
dc.identifier.uri en
dc.description Srinivasan, A., King, R. D. and Bain, M.E. (2003) An Empirical Study of the Use of Relevance Information in Inductive Logic Programming. Journal of Machine Learning Research. 4(Jul):369-383 en
dc.description.abstract Inductive Logic Programming (ILP) systems constructmodels for data using domain-specific background information. When using these systems, it is typically assumed that sufficient human expertise is at hand to rule out irrelevant background information. Such irrelevant information can, and typically does, hinder an ILP system’s search for good models. Here, we provide evidence that if expertise is available that can provide a partial-ordering on sets of background predicates in terms of relevance to the analysis task, then this can be used to good effect by an ILP system. In particular, using data from biochemical domains, we investigate an incremental strategy of including sets of predicates in decreasing order of relevance. Results obtained suggest that: (a) the incremental approach identifies, in substantially less time, a model that is comparable in predictive accuracy to that obtained with all background information in place; and (b) the incremental approach using the relevance ordering performs better than one that does not (that is, one that adds sets of predicates randomly). For a practitioner concerned with use of ILP, the implication of these findings are twofold: (1) when not all background information can be used at once (either due to limitations of the ILP system, or the nature of the domain) expert assessment of the relevance of background predicates can assist substantially in the construction of good models; and (2) good “first-cut” results can be obtained quickly by a simple exclusion of information known to be less relevant. en
dc.format.extent 15 en
dc.language.iso eng
dc.relation.ispartof Journal of Machine Learning Research en
dc.rights en
dc.subject ILP en
dc.subject relevance of background predicates en
dc.subject expert-assistance en
dc.title An Empirical Study of the Use of Relevance Information in Inductive Logic Programming en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Bioinformatics and Computational Biology Group en
dc.description.status Peer reviewed en

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