Meta-Feature Taxonomy for Supporting Automatic Machine Learning

Simple item page
dc.contributor.advisorMaurer, Frank
dc.contributor.advisorDenzinger, Jörg
dc.contributor.authorDavies, Cooper
dc.contributor.committeememberJacob, Christian
dc.contributor.committeememberAlim, Usman R.
dc.contributor.committeememberOehlberg, Lora A.
dc.date2020-02
dc.date.accessioned2020-01-02T06:48:45Z
dc.date.available2020-01-02T06:48:45Z
dc.date.issued2019-12-23
dc.description.abstractMany automatic machine learning (AutoML) libraries have been developed recently, meeting public demand for more machine learning tools which can be used without an expert. A common tactic illicited by these frameworks is to initially generate meta-features which are then used as an initial heuristic for further evaluation in recent AutoML frameworks. In this thesis we provide a systematic categorization of meta-features in the AutoML literature. Current implementations of automatic machine learning frameworks fail to provide reasoning for meta-feature selection, and a taxonomic categorization is needed. Our approach reviewed current AutoML frameworks and created a taxonomy of five categories into which any meta-feature can be categorized. We have created a general framework with which any currently used meta-features can be described, as well as demonstrate some scenarios for their applications. Additionally, a runtime analysis of the wall-clock time required for meta-feature generation is provided for 18 data collections found in previous CHALearn AutoML competitions, which took between 0:10:26.9, and 98:43:46.5. Additionally we found that a sample percentage of 0.1 is sufficient for use in Sample Variant Landmark Meta-Feature generation when using the Nearest Neighbour, Elite Nearest Neighbour, Best Decision Node, and Random Decision Node Landmarks which indicates potential use as meta-features in AutoML.en_US
dc.identifier.citationCooper T.S. Meta-Feature Taxonomy for Supporting Automatic Machine Learning (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/37377
dc.identifier.urihttp://hdl.handle.net/1880/111399
dc.language.isoengen_US
dc.publisher.facultyScienceen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectMachine Learningen_US
dc.subjectMeta-Featureen_US
dc.subjectAutomatic Machine Learningen_US
dc.subjectAutoMLen_US
dc.subject.classificationComputer Scienceen_US
dc.titleMeta-Feature Taxonomy for Supporting Automatic Machine Learningen_US
dc.typemaster thesisen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopytrueen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ucalgary_2019_davies_cooper.pdf
Size:
1.02 MB
Format:
Adobe Portable Document Format
Description:
Thesis
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Open Theses and Dissertations