Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent

arXiv:2403.04015v1 Announce Type: new
Abstract: Feature selection prepares the AI-readiness of data by eliminating redundant features. Prior research falls into two primary categories: i) Supervised Feature Selection, which identifies the optimal feature subset based on their relevance to the target variable; ii) Unsupervised Feature Selection, which reduces the feature space dimensionality by capturing the essential information within the feature set instead of using target variable. However, SFS approaches suffer from time-consuming processes and limited generalizability due to the dependence on …

abstract agent arxiv cs.ai cs.lg data dimensionality feature features feature selection prior research space stat.ml type unsupervised via