Authors:
Helene Dörksen
and
Volker Lohweg
Affiliation:
Ostwestfalen-Lippe University of Applied Sciences, Germany
Keyword(s):
Refinement of Classification, Robust Classification, Classification within Small/Incomplete Samples.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Hybrid Learning Algorithms
;
ICA, PCA, CCA and other Linear Models
;
Large Margin Methods
;
Learning in Process Automation
;
Object Recognition
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
Abstract:
In real-world scenarios it is not always possible to generate an appropriate number of measured objects for
machine learning tasks. At the learning stage, for small/incomplete datasets it is nonetheless often possible to
get high accuracies for several arbitrarily chosen classifiers. The fact is that many classifiers might perform
accurately, but decision boundaries might be inadequate. In this situation, the decision supported by marginlike
characteristics for the discrimination of classes might be taken into account. Accuracy as an exclusive
measure is often not sufficient. To contribute to the solution of this problem, we present a margin-based
approach originated from an existing refinement procedure. In our method, margin value is considered as
optimisation criterion for the refinement of SVM models. The performance of the approach is evaluated on
a real-world application dataset for Motor Drive Diagnosis coming from the field of intelligent autonomous
systems in the context of
Industry 4.0 paradigm as well as on several UCI Repository samples with different
numbers of features and objects.
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