Optimal Feature Selection via NSGA-II for Power Quality Disturbances Classification
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DOI: 10.1109/tii.2017.2773475
Abstract: This paper presents an application of nondominated sorting genetic algorithm II (NSGA-II) for multiobjective feature selection in power quality disturbances classification. Classification error and number of features are collectively minimized to ensure good accuracy and… read more here.
Keywords: classification; feature selection; power quality; quality disturbances ... See more keywords