2.50
Hdl Handle:
http://hdl.handle.net/10547/270597
Title:
An evolutionary-based approach to learning multiple decision models from underrepresented data
Authors:
Schetinin, Vitaly ( 0000-0003-1826-0153 ) ; Li, Dayou; Maple, Carsten
Abstract:
The use of multiple Decision Models (DMs) enables to enhance the accuracy in decisions and at the same time allows users to evaluate the confidence in decision making. In this paper we explore the ability of multiple DMs to learn from a small amount of verified data. This becomes important when data samples are difficult to collect and verify. We propose an evolutionary-based approach to solving this problem. The proposed technique is examined on a few clinical problems presented by a small amount of data.
Citation:
Schetinin, V., Li, D., Maple, C. (2008) An Evolutionary-Based Approach to Learning Multiple Decision Models from Underrepresented Data, The 4th International Conference on Natural Computation (ICNC'08), 1, pp.40-44
Publisher:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date:
2008
URI:
http://hdl.handle.net/10547/270597
DOI:
10.1109/ICNC.2008.409
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4666807
Type:
Conference papers, meetings and proceedings
Language:
en
ISBN:
9780769533049
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorSchetinin, Vitalyen_GB
dc.contributor.authorLi, Dayouen_GB
dc.contributor.authorMaple, Carstenen_GB
dc.date.accessioned2013-02-27T16:13:49Z-
dc.date.available2013-02-27T16:13:49Z-
dc.date.issued2008-
dc.identifier.citationSchetinin, V., Li, D., Maple, C. (2008) An Evolutionary-Based Approach to Learning Multiple Decision Models from Underrepresented Data, The 4th International Conference on Natural Computation (ICNC'08), 1, pp.40-44en_GB
dc.identifier.isbn9780769533049-
dc.identifier.doi10.1109/ICNC.2008.409-
dc.identifier.urihttp://hdl.handle.net/10547/270597-
dc.description.abstractThe use of multiple Decision Models (DMs) enables to enhance the accuracy in decisions and at the same time allows users to evaluate the confidence in decision making. In this paper we explore the ability of multiple DMs to learn from a small amount of verified data. This becomes important when data samples are difficult to collect and verify. We propose an evolutionary-based approach to solving this problem. The proposed technique is examined on a few clinical problems presented by a small amount of data.en_GB
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4666807en_GB
dc.titleAn evolutionary-based approach to learning multiple decision models from underrepresented dataen
dc.typeConference papers, meetings and proceedingsen
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