Context-sensitive Refinements for Stochastic Optimization Algorithms in Inductive Logic Programming

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00171012" target="_blank" >RIV/68407700:21230/10:00171012 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Context-sensitive Refinements for Stochastic Optimization Algorithms in Inductive Logic Programming
Original language description
In this paper we describe a new approach to the application of evolutionary stochastic search in Inductive Logic Programming (ILP). Unlike traditional approaches that focus on evolving populations of logical clauses, our refinement-based approach uses the stochastic optimization process to iteratively adapt initial working clause. Utilization of context-sensitive concept refinements (adaptations) helps the search operations to produce mostly syntactically correct concepts and enables using available background knowledge both for efficiently restricting the search space and for directing the search. Thereby, the search is more flexible, less problem-specific and the framework can be easily used with any stochastic search algorithm within ILP domain. Experimental results on several data sets verify the usefulness of this approach.
Czech name
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Czech description
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Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 12th annual conference comp on Genetic and evolutionary computation
ISBN
978-1-4503-0073-5
ISSN
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e-ISSN
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Number of pages
2
Pages from-to
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Publisher name
ACM
Place of publication
New York
Event location
Portland, Oregon
Event date
Jul 7, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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