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Stacking of network based classifiers with application in breast cancer classification

Sfakianakis Stylianos, Bei Aikaterini, Zervakis Michail

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URIhttp://purl.tuc.gr/dl/dias/461F1A76-FB1C-481C-823C-A91D78613D6C-
Identifierhttps://link.springer.com/chapter/10.1007/978-3-319-32703-7_214-
Identifierhttps://doi.org/10.1007/978-3-319-32703-7_214-
Languageen-
Extent6 pagesen
TitleStacking of network based classifiers with application in breast cancer classificationen
CreatorSfakianakis Stylianosen
CreatorΣφακιανακης Στυλιανοςel
CreatorBei Aikaterinien
CreatorΜπεη Αικατερινηel
CreatorZervakis Michailen
CreatorΖερβακης Μιχαηλel
PublisherSpringer Verlagen
Content SummaryIn this study we present the use of existing biological knowledge in the form of biological networks for the construction of a two level classification scheme. At the first level base classifiers are built using a given list of candidate “biomarkers” and the topology of the biological network. In particular, the network structure is taken into account by a search strategy based on random walks for the selection of the genes used in these classifiers. At the second level, a metaclassifier is trained to combine in the best possible way the results of the base classifiers. The proposed approach therefore aims to strengthen the classification ability of the initial list of genes and provide more robust generalization guarantees. Our methodology is explained in full detail and promising results in Breast Cancer related scenarios are presented.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2018-11-02-
Date of Publication2016-
SubjectBiological networksen
SubjectBreast canceren
SubjectEnsemble learningen
SubjectPage ranken
Bibliographic CitationS. Sfakianakis, E. S. Bei and M. Zervakis, "Stacking of network based classifiers with application in breast cancer classification," in 14th Mediterranean Conference on Medical and Biological Engineering and Computing, 2016, pp. 1079-1084. doi: 10.1007/978-3-319-32703-7_214en

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