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Minimizing Data Consumption with Sequential Online Feature Selection

Rückstieß, Thomas and Osendorfer, Christian and Smagt van der, Patrick (2012) Minimizing Data Consumption with Sequential Online Feature Selection. International Journal of Machine Learning and Cybernetics, April 2012. Springer. DOI: 10.1007/s13042-012-0092-x ISSN 1868-8071

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Abstract

In most real-world information processing problems, data is not a free resource. Its acquisition is often expensive and time-consuming. We investigate how such cost factors can be included in supervised classification tasks by deriving classification as a sequential decision process and making it accessible to reinforcement learning. Depending on previously selected features and the internal belief of the classifier, a next feature is chosen by a sequential online feature selection that learns which features are most informative at each time step. Experiments on toy datasets and a handwritten digits classification task show significant reduction in required data for correct classification, while a medical diabetes prediction task illustrates variable feature cost minimization as a further property of our algorithm

Item URL in elib:https://elib.dlr.de/81300/
Document Type:Article
Title:Minimizing Data Consumption with Sequential Online Feature Selection
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Rückstieß, Thomas Technische Universität MünchenUNSPECIFIED
Osendorfer, Christian Technische Universität MünchenUNSPECIFIED
Smagt van der, PatrickUNSPECIFIEDUNSPECIFIED
Date:2012
Journal or Publication Title:International Journal of Machine Learning and Cybernetics
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:April 2012
DOI :10.1007/s13042-012-0092-x
Publisher:Springer
ISSN:1868-8071
Status:Published
Keywords:Reinforcement learning, Feature selection, Classification
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Terrestrische Assistenz-Robotik
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (until 2012)
Deposited By: Grebenstein, Dr. sc. Markus
Deposited On:21 Feb 2013 13:37
Last Modified:06 Sep 2019 15:30

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