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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| Vydané v: | 2020 IEEE Congress on Evolutionary Computation (CEC) s. 1 - 8 |
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| Hlavní autori: | , |
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| Jazyk: | English |
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01.07.2020
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Topcuoglu, Haluk Rahmi Sahmoud, Shaaban |
| Author_xml | – sequence: 1 givenname: Shaaban surname: Sahmoud fullname: Sahmoud, Shaaban organization: Fatih Sultan Mehmet Vakif University,Computer Engineering Department,Istanbul,Turkey – sequence: 2 givenname: Haluk Rahmi surname: Topcuoglu fullname: Topcuoglu, Haluk Rahmi organization: Marmara University,Computer Engineering Department,Istanbul,Turkey |
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| Snippet | 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... |
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| SubjectTerms | 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 |
| Title | Memory-Assisted Dynamic Multi-Objective Evolutionary Algorithm for Feature Drift Problem |
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