Neuro-Symbolic Embedding for Short and Effective Feature Selection via Autoregressive Generation

arXiv:2404.17157v1 Announce Type: new
Abstract: Feature selection aims to identify the optimal feature subset for enhancing downstream models. Effective feature selection can remove redundant features, save computational resources, accelerate the model learning process, and improve the model overall performance. However, existing works are often time-intensive to identify the effective feature subset within high-dimensional feature spaces. Meanwhile, these methods mainly utilize a single downstream task performance as the selection criterion, leading to the selected subsets that are not only redundant but …

abstract arxiv autoregressive computational cs.lg embedding feature features feature selection however identify neuro performance process resources save type via