A New Genetic Algorithm for Multi-Label Correlation-Based Feature Selection
نویسنده
چکیده
This paper proposes a new Genetic Algorithm for Multi-Label Correlation-Based Feature Selection (GA-ML-CFS). This GA performs a global search in the space of candidate feature subsets, in order to select a high-quality feature subset that is used by a multi-label classification algorithm – in this work, the Multi-Label k-NN algorithm. We compare the results of GA-ML-CFS with the results of the previously proposed Hill-Climbing for Multi-Label CorrelationBased Feature Selection (HC-ML-CFS), across 10 multi-label datasets.
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تاریخ انتشار 2014