Ensembles and PMML in KNIME
| Type of Publication: | Contribution to a conference |
| Publication status: | Published |
| URI (citable link): | http://nbn-resolving.de/urn:nbn:de:bsz:352-0-394245 |
| Author: | Fillbrunn, Alexander; Adä, Iris; Gabriel, Thomas R.; Berthold, Michael R. |
| Year of publication: | 2013 |
| Conference: | PMML Workshop, Aug 10, 2013, Chicago |
| Published in: | PMML Workshop Proceedings : KDD 2013. - New York, NY : ACM, 2013. - ISBN 978-1-4503-2573-8 |
| URL of original publication: | http://chbrown.github.io/kdd-2013-usb/workshops/PMML/docs/EnsemblePMML.pdf, Last access on Apr 24, 2017 |
| Summary: |
In this paper we describe how ensembles can be trained, modified and applied in the open source data analysis platform, KNIME. We focus on recent extensions that also allow ensembles, represented in PMML, to be processed. This way ensembles generated in KNIME can be deployed to PMML scoring engines. In addition ensembles created by other tools and represented as PMML can be applied or further processed (modified or filtered) using intuitive KNIME workflows.
|
| Subject (DDC): | 004 Computer Science |
| Keywords: | PMML, Ensemble Models, KNIME |
| Link to License: | Terms of use |
| Bibliography of Konstanz: | Yes |
Cite This
Files in this item
![[PDF]](/themes/Kops/images/mimes/pdf.png "PDF file"){kind=small}
FILLBRUNN, Alexander, Iris ADÄ, Thomas R. GABRIEL, Michael R. BERTHOLD, 2013. Ensembles and PMML in KNIME. PMML Workshop. Chicago, Aug 10, 2013. In: PMML Workshop Proceedings : KDD 2013. New York, NY:ACM. ISBN 978-1-4503-2573-8
@inproceedings{Fillbrunn2013Ensem-38103, title={Ensembles and PMML in KNIME}, url={http://chbrown.github.io/kdd-2013-usb/workshops/PMML/docs/EnsemblePMML.pdf}, year={2013}, isbn={978-1-4503-2573-8}, address={New York, NY}, publisher={ACM}, booktitle={PMML Workshop Proceedings : KDD 2013}, author={Fillbrunn, Alexander and Adä, Iris and Gabriel, Thomas R. and Berthold, Michael R.} }
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/38103"> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-03-22T15:58:56Z</dc:date> <dc:contributor>Berthold, Michael R.</dc:contributor> <dcterms:rights rdf:resource="https://kops.uni-konstanz.de/page/termsofuse"/> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/38103/1/Fillbrunn_0-394245.pdf"/> <dcterms:title>Ensembles and PMML in KNIME</dcterms:title> <dc:contributor>Adä, Iris</dc:contributor> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:creator>Berthold, Michael R.</dc:creator> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:creator>Adä, Iris</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-03-22T15:58:56Z</dcterms:available> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/38103/1/Fillbrunn_0-394245.pdf"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:language>eng</dc:language> <dc:contributor>Fillbrunn, Alexander</dc:contributor> <dc:creator>Fillbrunn, Alexander</dc:creator> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/38103"/> <dcterms:issued>2013</dcterms:issued> <dcterms:abstract xml:lang="eng">In this paper we describe how ensembles can be trained, modified and applied in the open source data analysis platform, KNIME. We focus on recent extensions that also allow ensembles, represented in PMML, to be processed. This way ensembles generated in KNIME can be deployed to PMML scoring engines. In addition ensembles created by other tools and represented as PMML can be applied or further processed (modified or filtered) using intuitive KNIME workflows.</dcterms:abstract> <dc:rights>terms-of-use</dc:rights> <dc:contributor>Gabriel, Thomas R.</dc:contributor> <dc:creator>Gabriel, Thomas R.</dc:creator> </rdf:Description> </rdf:RDF>
Downloads since Mar 22, 2017 (Information about access statistics)
| Fillbrunn_0-394245.pdf | 313 |
This item appears in the following Collection(s)
Search KOPS
Browse
My Account
