Multivariate Correlation Analysis for Supervised Feature Selection in High-Dimensional Data
Multivariate Correlation Analysis for Supervised Feature Selection in High-Dimensional Data
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dc.contributor.advisor | Müller, Emmanuel | |
dc.contributor.author | Shekar, Arvind Kumar | |
dc.date.accessioned | 2020-04-27T16:04:18Z | |
dc.date.available | 2020-04-27T16:04:18Z | |
dc.date.issued | 12.03.2020 | |
dc.identifier.uri | https://hdl.handle.net/20.500.11811/8303 | |
dc.description.abstract | The main theme of this dissertation focuses on multivariate correlation analysis on different data types and we identify and define various research gaps in the same. For the defined research gaps we develop novel techniques that address relevance of features to the target and redundancy of features amidst themselves. Our techniques aim at handling homogeneous data, i.e., only continuous or categorical features, mixed data, i.e., continuous and categorical features, and time series | |
dc.language.iso | eng | |
dc.rights | In Copyright | |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject | Feature-Selektion | |
dc.subject | multivariate Korrelationsanalyse | |
dc.subject | Feature-Redundanz | |
dc.subject | mehrdimensionalen Wechselwirkungen | |
dc.subject | erklärbare Korrelationen | |
dc.subject | Ordinalmuster | |
dc.subject | kontinuierliche und kategorische Daten | |
dc.subject | Zeitreihenanalyse | |
dc.subject | filterbasierte Ansätze | |
dc.subject | feature selection | |
dc.subject | higher-order interactions | |
dc.subject | redundancy | |
dc.subject | filter-based approach | |
dc.subject | ordinal patterns | |
dc.subject | feature extraction | |
dc.subject | explainable correlations | |
dc.subject.ddc | 004 Informatik | |
dc.title | Multivariate Correlation Analysis for Supervised Feature Selection in High-Dimensional Data | |
dc.type | Dissertation oder Habilitation | |
dc.publisher.name | Universitäts- und Landesbibliothek Bonn | |
dc.publisher.location | Bonn | |
dc.rights.accessRights | openAccess | |
dc.identifier.urn | https://nbn-resolving.org/urn:nbn:de:hbz:5-58003 | |
ulbbn.pubtype | Erstveröffentlichung | |
ulbbnediss.affiliation.name | Rheinische Friedrich-Wilhelms-Universität Bonn | |
ulbbnediss.affiliation.location | Bonn | |
ulbbnediss.thesis.level | Dissertation | |
ulbbnediss.dissID | 5800 | |
ulbbnediss.date.accepted | 11.12.2019 | |
ulbbnediss.institute | Zentrale wissenschaftliche Einrichtungen : Bonn-Aachen International Center for Information Technology (b-it) | |
ulbbnediss.fakultaet | Mathematisch-Naturwissenschaftliche Fakultät | |
dc.contributor.coReferee | Hüllermeier, Eyke |
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