Beschreibung
A graph-based, semi-supervised learning algorithmic rule called SHG-Health (Semi-supervised Heterogeneous Graph on Health) for risk predictions to classify a progressively developing situation with the majority of the data unlabeled. An efficient iterative algorithm is designed, and the proof of convergence is given. Extensive experiments based on both real health examination datasets and synthetic datasets are performed to show the effectiveness and efficiency of this method.
Autorenporträt
Dr. K. Sasi Kala Rani is working as a professor and the head of the department of computer science and engineering. She has 16 years of teaching experience, and she has published many papers in Scopus and WOS.
