Abstract
This paper presents a feature selection method for Internet of Things (IoT) information processing, called MIMIC_FS. The maximal information coefficient (MIC), which can capture a wide range of correlations of variables, including linear, nonlinear, and nonfunctional correlations, is introduced to measure the relevance and redundancy between features and class labels. Based on this measure, the MIC-relevance-average-MIC-redundancy criterion is presented to evaluate the goodness of features, and an approximate-Markov-blanket search strategy is then proposed to improve the efficiency of feature selection. To validate the present study, MIC is also applied to feature selection directly by using the feature-ranking strategy. Experiments on six ASU datasets for IoT applications were conducted. The results show that the proposed method achieves better performance than the comparison methods, markedly reducing feature dimensionality in order to process the tremendous quantities of data in IoT.
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