A Visual Interface for Feature Subset Selection Using Machine Learning Methods

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dc.contributor.authorRojo, Diegoen_US
dc.contributor.authorRaya, Lauraen_US
dc.contributor.authorRubio-Sánchez, Manuelen_US
dc.contributor.authorSánchez, Albertoen_US
dc.contributor.editorGarcía-Fernández, Ignacio and Ureña, Carlosen_US
dc.date.accessioned2018-06-26T13:59:39Z
dc.date.available2018-06-26T13:59:39Z
dc.date.issued2018
dc.description.abstractVisual representation of information remains a key part of exploratory data analysis. This is due to the high number of features in datasets and their increasing complexity, together with users' ability to visually understand information. One of the most common operations in exploratory data analysis is the selection of relevant features in the available data. In multidimensional scenarios, this task is often done with the help of automatic dimensionality reduction algorithms from the machine learning field. In this paper we develop a visual interface where users are integrated into the feature selection process of several machine learning algorithms. Users can work interactively with the algorithms in order to explore the data, compare the results and make the appropriate decisions about the feature selection process.en_US
dc.description.sectionheadersData Analysis and Visualization
dc.description.seriesinformationSpanish Computer Graphics Conference (CEIG)
dc.identifier.doi10.2312/ceig.20181165
dc.identifier.isbn978-3-03868-067-3
dc.identifier.pages119-128
dc.identifier.urihttps://doi.org/10.2312/ceig.20181165
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/ceig20181165
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectVisualization systems and tools
dc.subjectComputing methodologies
dc.subjectFeature selection
dc.titleA Visual Interface for Feature Subset Selection Using Machine Learning Methodsen_US
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