A general framework based on dynamic multi-objective evolutionary algorithms for handling feature drifts on data streams

This paper proposes a new and efficient framework to deal with the classification of data streams when exhibiting feature drifts. The first building block of the framework is a dynamic multi-objective evolutionary algorithm called Dynamic Filter-Based Feature Selection (DFBFS) algorithm, which handl...

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Veröffentlicht in:	Future generation computer systems Jg. 102; S. 42 - 52
Hauptverfasser:	Sahmoud, Shaaban, Topcuoglu, Haluk Rahmi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.01.2020
Schlagworte:	Classification of data streams Dynamic multi-objective evolutionary algorithms Feature drift Filter-based feature selection Classification of data streams Dynamic multi-objective evolutionary algorithms Feature drift Filter-based feature selection
ISSN:	0167-739X, 1872-7115
Online-Zugang:	Volltext
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