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...

Celý popis

Podrobná bibliografie

Vydáno v:	Future generation computer systems Ročník 102; s. 42 - 52
Hlavní autoři:	Sahmoud, Shaaban, Topcuoglu, Haluk Rahmi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.01.2020
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!