FMLFS: A federated multi-label feature selection based on information theory in IoT environment

arXiv:2405.00524v1 Announce Type: new
Abstract: In certain emerging applications such as health monitoring wearable and traffic monitoring systems, Internet-of-Things (IoT) devices generate or collect a huge amount of multi-label datasets. Within these datasets, each instance is linked to a set of labels. The presence of noisy, redundant, or irrelevant features in these datasets, along with the curse of dimensionality, poses challenges for multi-label classifiers. Feature selection (FS) proves to be an effective strategy in enhancing classifier performance and addressing these …

abstract applications arxiv cs.it cs.lg cs.ni datasets devices environment feature feature selection generate health information instance internet iot labels math.it monitoring set systems theory traffic type wearable