Efficient Construction of Relational Features

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F05%3A03114946" target="_blank" >RIV/68407700:21230/05:03114946 - 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
Efficient Construction of Relational Features
Original language description
Devising algorithms for learning from multi-relational data is currently considered an important challenge. The wealth of traditional single-relational machine learning tools, on the other hand, calls for methods of `propositionalization', ie. Conversionof multi-relational data into single-relational representations. A major stream of propositionalization algorithms is based on the construction of truth-valued features (first-order logic atom conjunctions), which capture relational properties of data and play the role of binary attributes in the resulting single-table representation. Such algorithms typically use backtrack depth first search for the syntactic construction of features complying to user's mode/type declarations. As such they incur a complexity factor exponential in the maximum allowed feature size.
Czech name
Není k dispozici
Czech description
Není k dispozici

Publication year
2005
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
4th International Conference on Machine Learning and Applications - Proceedings
ISBN
0-7695-2495-8
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
259-264
Publisher name
IEEE Computer Society Press
Place of publication
Los Alamitos
Event location
Los Angeles, California
Event date
Dec 15, 2005
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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