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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Bibliographic Details

Published in:	Future generation computer systems Vol. 102; pp. 42 - 52
Main Authors:	Sahmoud, Shaaban, Topcuoglu, Haluk Rahmi
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.01.2020
Subjects:	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 Access:	Get full text
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