<div class="csl-bib-body">
<div class="csl-entry">Lepadat, M.-A. (2019). <i>Rule-based recommender for feature engineering in big data</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.65802</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2019.65802
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/13802
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dc.description
Abweichender Titel nach Übersetzung der Verfasserin/des Verfassers
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dc.description.abstract
Feature engineering is of high importance for the success of many machine learning algorithms and requires domain-specific knowledge. Generally, this knowledge is only familiar to domain experts or incorporated into programs. We developed a knowledge-driven approach to support users during feature engineering and implemented a software application to evaluate this approach. The knowledge is represented in Web Ontology Language (OWL) and its main purpose is to offer the user a flexible way to tackle domain-specific datasets by building a reusable and comprehensible knowledge base. A semantic reasoner makes use of this knowledge to infer properties and provide users with recommendations. All data-related operations are performed in a scalable cluster computing engine backed up by Apache Spark. The evaluation is done on 6 freely available datasets from the domain of demographics. We were able to identify only a small fraction of recommendations that proved to be wrong.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Feature Engineering
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dc.subject
Recommender
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dc.subject
Machine Learning
en
dc.subject
Apache Spark
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dc.subject
Ontology
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dc.title
Rule-based recommender for feature engineering in big data
en
dc.title.alternative
Regelbasierte Empfehlungen für Feature Engineering in großen Datenmengen
de
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2019.65802
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Mihai-Alexandru Lepadat
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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dc.contributor.assistant
Knees, Peter
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tuw.publication.orgunit
E194 - Institut für Information Systems Engineering