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In this study, a hybrid genetic algorithm is adopted to find a subset of features that are most relevant to the classification task. Two stages of optimization are involved. The outer optimization stage completes the global search for the best subset of features in a wrapper way, in which the mutual information between the predictive labels of a trained classifier and the true classes serves as the...
This paper adopts a wrapper method to find a subset of features that are most relevant to the classification task. The approach utilizes an improved estimation of the conditional mutual information which is used as an independent measure for feature ranking in the local search operations. Meanwhile, the mutual information between the predictive labels of a trained classifier and the true classes is...
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