An Evolutionary-Based Approach to Learning Multiple Decision Models from Underrepresented Data

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dc.creatorSchetinin, Vitaly
dc.creatorLi, Dayou
dc.creatorMaple, Carsten
dc.date2008-05-24
dc.date.accessioned2026-07-07T09:40:50Z
dc.date.available2026-07-07T09:40:50Z
dc.descriptionThe 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.
dc.description5 pages, 3 figures, 2 tables, The 4 th International Conference on Natural Computation (ICNC'08)
dc.identifierhttps://arxiv.org/abs/0805.3800
dc.identifierhttp://arxiv.org/abs/0805.3800
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/161615
dc.subjectArtificial Intelligence
dc.subjectNeural and Evolutionary Computing
dc.titleAn Evolutionary-Based Approach to Learning Multiple Decision Models from Underrepresented Data
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