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Margin-based Refinement for Linear Discriminant Analysis

Dörksen, Helene; Lohweg, Volker

Abstract:
For the two classes supervised learning problem, we present a refinement method for increasing the classification accuracy of an initial separating hyperplane in the feature space Rd. The main idea corresponds to dimensionality reduction of, e.g. LDA separation, however not in its original form R$^{d}$ ! R but rather as dimensionality reduction R$^{d}$ ! R$^{j}$ for some j < d and j > 1. The method combines discriminant and margin-based properties of the separation. Due to efficiency reasons, we define rules for fast calculation of the refinement. Furthermore, we discuss theoretical fundamentals of our method and show its high performance by cross-validation tests on datasets from the UCI Machine Learning Repository with different numbers of features and objects. Due to the margin-based origin, the method is suitable for not well-balanced datasets. Cross-validation tests for not well-balanced data are given as well.

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Verlagsausgabe §
DOI: 10.5445/KSP/1000085951/12
Veröffentlicht am 29.04.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000118784
Erschienen in Archives of Data Science, Series A (Online First)
Band 4
Heft 1
Seiten A12, 21 S. online
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