Feature Selection as Deep Sequential Generative Learning

arXiv:2403.03838v1 Announce Type: new
Abstract: Feature selection aims to identify the most pattern-discriminative feature subset. In prior literature, filter (e.g., backward elimination) and embedded (e.g., Lasso) methods have hyperparameters (e.g., top-K, score thresholding) and tie to specific models, thus, hard to generalize; wrapper methods search a feature subset in a huge discrete space and is computationally costly. To transform the way of feature selection, we regard a selected feature subset as a selection decision token sequence and reformulate feature selection …

abstract arxiv cs.lg embedded feature feature selection filter generative identify lasso literature prior search space thresholding type wrapper