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In this paper, we present a novel embedded feature selection method based ona Multi-layer Perceptron (MLP) network and generalize it for group-feature orsensor selection problems, which can control the level of redundancy among theselected features or groups. Additionally, we have generalized the group lassopenalty for feature selection to encompass a mechanism for selecting valuablegroup features while simultaneously maintaining a control over redundancy. Weestablish the monotonicity and convergence of the proposed algorithm, with asmoothed version of the penalty terms, under suitable assumptions. Experimentalresults on several benchmark datasets demonstrate the promising performance ofthe proposed methodology for both feature selection and group feature selectionover some state-of-the-art methods.

Group-Feature (Sensor) Selection With Controlled Redundancy Using Neural Networks