To solve the problem that redundancy or irrelevant features in high-dimensional datasets reduce the classification accuracy of machine learning model,a feature selection algorithm based on approximate Markov blanket is proposed and named as normal max-relevance and min-redundancy(nmRMR)algorithm.Firstly,the algorithm uses the criteria of maximum relevance and minimum redundancy to perform feature relevance ranking.Then,it adopts the approximate Markov blanket to remove redundant features or irrelevant featu...
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