With the explosive development of communication technologies, more customer friendly services have been designed for the next generation of cellular technology, that is, fifth-generation (5G) communication. However, such services require more computing resources and energy.
Thus, the development of green and energy-efficient 5G application systems has become an important topic in communications. In this paper, we focus on high-performance multi-label classification methods and their application for medical recommendations in the domain of 5G communication.
In machine learning, multi-label classification involves assigning multiple target labels to each query instance. The vast number of labels poses a challenge for maintaining efficiency. Several related approaches have been proposed to meet this challenge. In this paper, we propose two label selection methods for multi-label classification: clustering-based sampling and frequency-based sampling.
We apply our proposed multi-label classification methods as an innovative 5G application to predict doctor labels for doctor recommendations. We perform experiments on real-world data sets. The experimental results show that our methods achieve the state-of-the-art performance compared with baselines. In addition, we develop a mobile application of a doctor recommendation system based on our proposed methods.
Authors: Li Guo | Bo Jin | Ruiyun Yu | Cuili Yao | Chonglin Sun | Degen Huang