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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Vydáno v:2020 IEEE Congress on Evolutionary Computation (CEC) s. 1 - 8
Hlavní autoři: Sahmoud, Shaaban, Topcuoglu, Haluk Rahmi
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2020
Témata:
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
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Shrnutí: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 relevant features after the occurrence of every change in the environment. An artificial neural network is employed to classify the new instances based on the up-to-date obtained set of relevant features efficiently. Our algorithm exploits a detection mechanism for the severity of changes to estimate the severity level of occurred changes and adaptively replies to these changes by introducing diversity to algorithm solutions. Furthermore, a fixed-size memory is used to store the good solutions and reuse them after each change to accelerate the convergence and searching process of the algorithm. The experimental results using three datasets and different environmental parameters show that the combination of our improved feature selection algorithm with the artificial neural network outperforms related work.
DOI:10.1109/CEC48606.2020.9185730