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

Abstract

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.