Group-Feature (Sensor) Selection With Controlled Redundancy Using Neural Networks
This work addresses feature selection with redundancy control, which is important for improving model interpretability and efficiency in machine learning applications, though it appears incremental as it builds on existing group lasso penalties.
The paper tackles the problem of selecting features or sensor groups while controlling redundancy, introducing a novel embedded feature selection method based on a Multi-layer Perceptron network. Experimental results show promising performance over state-of-the-art methods on benchmark datasets.
In this paper, we present a novel embedded feature selection method based on a Multi-layer Perceptron (MLP) network and generalize it for group-feature or sensor selection problems, which can control the level of redundancy among the selected features or groups. Additionally, we have generalized the group lasso penalty for feature selection to encompass a mechanism for selecting valuable group features while simultaneously maintaining a control over redundancy. We establish the monotonicity and convergence of the proposed algorithm, with a smoothed version of the penalty terms, under suitable assumptions. Experimental results on several benchmark datasets demonstrate the promising performance of the proposed methodology for both feature selection and group feature selection over some state-of-the-art methods.