Machine learning methods for ordinal classification with additional relative information
The ability to learn an accurate classification process is often limited by the amount of labeled data. Incorporating additional information into the learning process for overcoming this limitation has been a popular research topic. In this work, we focus on ordinal classification problems that are provided with limited absolute information and additional relative information. We modify and propose some machine learning methods, test their performances for ordinal classification.
Mengzi Tang received the master degree in computer science from Southwest University, Chongqing, China in 2017. She is currently pursuing the PhD degree with the Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium. Her current research interests include data mining, machine learning and their applications.