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Verlagsausgabe
DOI: 10.5445/KSP/1000058749/29
Veröffentlicht am 16.01.2018

Ordinal Prototype-Based Classifiers

Burkovski, Andre; Schirra, Lyn-Rouven; Schmid, Florian; Lausser, Ludwig; Kestler, Hans A.

Abstract:
The identification of prototypical patterns is one of the major goals in the classification of microarray data. Prototype-based classifiers are of special interest in this context, since they allow a direct biological interpretation. In this work we present prototype-based classifiers that rely on ordinal-scaled data. Advantage of these ordinal-scaled signatures is their invariance to a wide range of data transformations. Standard prototype-based classifiers can be modified to this type of data by utilizing rank-distances and rank-aggregation procedures. In this study, we compare the proposed methods with standard classifiers. They are examined in experiments with and without feature selection on a panel of publicly available microarray datasets. We show that the proposed techniques result in the construction of different signatures that improve classification performance.


Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2017
Sprache Englisch
Identifikator ISSN: 2363-9881
URN: urn:nbn:de:swb:90-789640
KITopen ID: 1000078964
Erschienen in Archives of Data Science, Series A (Online First)
Band 2
Heft 1
Seiten 35 S. online
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