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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.

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Verlagsausgabe §
DOI: 10.5445/KSP/1000058749/29
Veröffentlicht am 16.01.2018
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2017
Sprache Englisch
Identifikator ISSN: 2363-9881
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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