Memory-Assisted Dynamic Multi-Objective Evolutionary Algorithm for Feature Drift Problem

In this paper, we propose an enhanced feature selection algorithm able to cope with feature drift problem that may occur in data streams, where the set of relevant features change over time. We utilize a dynamic multi-objective evolutionary algorithm to continuously search for the updated set of rel...

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Veröffentlicht in:	2020 IEEE Congress on Evolutionary Computation (CEC) S. 1 - 8
Hauptverfasser:	Sahmoud, Shaaban, Topcuoglu, Haluk Rahmi
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.07.2020
Schlagworte:	dynamic multi-objective evolutionary algorithms Evolutionary computation feature drift Feature extraction Heuristic algorithms learning in non-stationary environments memory-based algorithms Optimization Power system dynamics severity of changes Sociology Statistics
Online-Zugang:	Volltext
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