A-SFS: Semi-supervised feature selection based on multi-task self-supervision

Feature selection is an important process in To this end, we innovatively introduces a deep learning-based self-supervised mechanism into feature selection problems, namely batch-Attention-based Self-supervision Feature Selection(A-SFS). Firstly, a multi-task self-supervised is designed to uncover the hidden structural among features with the support of two pretext tasks. Guided by the integrated information from the multi-self-supervised learning model, a batch-attention mechanism is designed to generate feature weights according to batch-based feature selection patterns to alleviate the impacts introduced from a handful of noisy data. This method is compared to 14 major strong benchmarks, including LightGBM and XGBoost. Experimental results show that A-SFS achieves the highest accuracy in most datasets. Furthermore, this design significantly reduces the reliance on labels, with only 1/10 labeled data are needed to achieve the same performance as those state of art baselines. Results show that A-SFS is also most robust to the noisy and missing data.