Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Cibulski, Lena | |
dc.contributor.author | May, Thorsten | |
dc.contributor.author | Preim, Bernhard | |
dc.contributor.author | Bernard, Jürgen | |
dc.contributor.author | Kohlhammer, Jörn | |
dc.contributor.editor | Skala, Václav | |
dc.date.accessioned | 2019-10-22T05:58:51Z | |
dc.date.available | 2019-10-22T05:58:51Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Journal of WSCG. 2019, vol. 27, no. 2, p. 93-102. | en |
dc.identifier.issn | 1213-6964 (on-line) | |
dc.identifier.issn | 1213-6972 (print) | |
dc.identifier.issn | 1213-6980 (CD-ROM) | |
dc.identifier.uri | http://hdl.handle.net/11025/35593 | |
dc.format | 10 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Václav Skala - UNION Agency | cs |
dc.rights | © Václav Skala - UNION Agency | cs |
dc.subject | vizuální analytika | cs |
dc.subject | výběr funkce | cs |
dc.subject | konzistence | cs |
dc.subject | vícerozměrné časové řady | cs |
dc.subject | model-agnostic | cs |
dc.subject | předpovídání | cs |
dc.title | Visualizing Time Series Consistency for Feature Selection | en |
dc.type | článek | cs |
dc.type | article | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.description.abstract-translated | Feature selection is an effective technique to reduce dimensionality, for example when the condition of a system is to be understood from multivariate observations. The selection of variables often involves a priori assumptions about underlying phenomena. To avoid the associated uncertainty, we aim at a selection criterion that only considers the observations. For nominal data, consistency criteria meet this requirement: a variable subset is consistent, if no observations with equal values on the subset have different output values. Such a model-agnostic criterion is alsodesirable for forecasting. However, consistency has not yet been applied to multivariate time series. In this work, we propose a visual consistency-based technique for analyzing a time series subset’s discriminating ability w.r.t. characteristics of an output variable. An overview visualization conveys the consistency of output progressions associated with comparable observations. Interaction concepts and detail visualizations provide a steering mechanism towards inconsistencies. We demonstrate the technique’s applicability based on two real-world scenarios. The results indicate that the technique is open to any forecasting task that involves multivariate time series, because analysts could assess the combined discriminating ability without any knowledge about underlying phenomena. | en |
dc.subject.translated | visual analytics | en |
dc.subject.translated | feature selection | en |
dc.subject.translated | consistency | en |
dc.subject.translated | multivariate time series | en |
dc.subject.translated | model-agnostic | en |
dc.subject.translated | forecasting | en |
dc.identifier.doi | https://doi.org/10.24132/JWSCG.2019.27.2.2 | |
dc.type.status | Peer-reviewed | en |
Vyskytuje se v kolekcích: | Volume 27, Number 2 (2019) |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
---|---|---|---|---|
Cibulski.pdf | Plný text | 10,38 MB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/35593
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