ML-KnockoffGAN: Deep online feature selection for multi-label learning

Many online platforms now generate data in a streaming manner, resulting in the continuous production of new features. Multi-label data generation has also surged in recent years, making feature selection for online multi-label data essential. However, existing feature selection methods are mainly based on single-label data or offline selection approaches. Only a few methods exist for multi-label data in an online framework, and most of these methods use classical or evolutionary-based techniques, paying little attention to deep learning. In this study, we propose a novel deep-learning feature selection technique that utilizes generative adversarial nets (GANs). We develop a framework, called ML-KnockoffGAN, which generates knockoff features in a multi-label setting, and then features are selected by considering both the generated knockoff features and real features together. As the features arrive online in a continuous fashion, our proposed method incorporates online features and selects them in a group-wise manner. We tested our method on various multi-label data sets from different domains, including text, biology, and audio, and our results show that our approach outperforms existing methods, with an average improvement of 7.1-16.3% for all evaluation metrics. Our method also illustrates the benefits of deep learning techniques in utilizing existing trained parameters to train new windows of features, requiring fewer epochs.(c) 2023 Elsevier B.V. All rights reserved.