dc.contributor.author |
Keet, CM
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|
dc.contributor.author |
Lawrynowicz, A
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|
dc.contributor.author |
D'Amato, C
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|
dc.contributor.author |
Hilario, M
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dc.date.accessioned |
2014-05-06T12:33:34Z |
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dc.date.available |
2014-05-06T12:33:34Z |
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dc.date.issued |
2013-05 |
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dc.identifier.citation |
Keet, C.M, Lawrynowicz, A, d’Amato, C and Hilario, M. 2013. Modeling issues & choices in the data mining optimization ontology. In: 8th Workshop on OWL: Experiences and Directions (OWLED'13), 26-27 May 2013, Montpellier, France |
en_US |
dc.identifier.uri |
http://webont.org/owled/2013/papers/owled2013_10.pdf
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|
dc.identifier.uri |
http://hdl.handle.net/10204/7390
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|
dc.description |
8th Workshop on OWL: Experiences and Directions (OWLED'13), 26-27 May 2013, Montpellier, France |
en_US |
dc.description.abstract |
We describe the Data Mining Optimization Ontology (DMOP), which was developed to support informed decision-making at various choice points of the knowledge discovery (KD) process. It can be used as a reference by data miners, but its primary purpose is to automate algorithm and model selection through semantic meta-mining, i.e., ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. DMOP contains in-depth descriptions of DM tasks (e.g., learning, feature selection), data, algorithms, hypotheses (mined models or patterns), and workflows. Its development raised a number of non-trivial modeling problems, the solution to which demanded maximal exploitation of OWL 2 representational potential. We discuss a number of modeling issues encountered and the choices made that led to version 5.3 of the DMOP ontology. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
CRC Press |
en_US |
dc.relation.ispartofseries |
Workflow;12377 |
|
dc.subject |
Meta-learning |
en_US |
dc.subject |
Data Mining Optimization Ontology |
en_US |
dc.subject |
DMOP |
en_US |
dc.subject |
DMOP Ontology |
en_US |
dc.title |
Modeling issues & choices in the data mining optimization ontology |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Keet, C., Lawrynowicz, A., D'Amato, C., & Hilario, M. (2013). Modeling issues & choices in the data mining optimization ontology. CRC Press. http://hdl.handle.net/10204/7390 |
en_ZA |
dc.identifier.chicagocitation |
Keet, CM, A Lawrynowicz, C D'Amato, and M Hilario. "Modeling issues & choices in the data mining optimization ontology." (2013): http://hdl.handle.net/10204/7390 |
en_ZA |
dc.identifier.vancouvercitation |
Keet C, Lawrynowicz A, D'Amato C, Hilario M, Modeling issues & choices in the data mining optimization ontology; CRC Press; 2013. http://hdl.handle.net/10204/7390 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Keet, CM
AU - Lawrynowicz, A
AU - D'Amato, C
AU - Hilario, M
AB - We describe the Data Mining Optimization Ontology (DMOP), which was developed to support informed decision-making at various choice points of the knowledge discovery (KD) process. It can be used as a reference by data miners, but its primary purpose is to automate algorithm and model selection through semantic meta-mining, i.e., ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. DMOP contains in-depth descriptions of DM tasks (e.g., learning, feature selection), data, algorithms, hypotheses (mined models or patterns), and workflows. Its development raised a number of non-trivial modeling problems, the solution to which demanded maximal exploitation of OWL 2 representational potential. We discuss a number of modeling issues encountered and the choices made that led to version 5.3 of the DMOP ontology.
DA - 2013-05
DB - ResearchSpace
DP - CSIR
KW - Meta-learning
KW - Data Mining Optimization Ontology
KW - DMOP
KW - DMOP Ontology
LK - https://researchspace.csir.co.za
PY - 2013
T1 - Modeling issues & choices in the data mining optimization ontology
TI - Modeling issues & choices in the data mining optimization ontology
UR - http://hdl.handle.net/10204/7390
ER -
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en_ZA |