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To enable stable feature selection from a vast number of behavioral features, Chikersal et al [29] proposed an approach called “nested randomized logistic regression,” which we deployed in this study. This method decomposed the feature space for each sensor by grouping features from the same time slices and performed randomized logistic regression on each of these groups. The selected features from all groups (ie, all time slices) are then concatenated to give a new and much smaller set of features.
JMIR Ment Health 2022;9(8):e38495
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Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning: Pilot Study
JMIR Form Res 2022;6(4):e35803
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