https://www.sciencedaily.com/releases/2023/02/230201084349.htm

Conventional machine learning mainly deals with single-label classification problems, in which data and corresponding phenomena or objects (label information) are in a one-to-one relationship. However, in the real world, data and label information rarely have a one-to-one relationship. In recent years, therefore, attention has focused on the multi-label classification problem, which deals with data that has a one-to-many relationship between data and label information. For example, a single landscape photo may include multiple labels for elements such as sky, mountains, and clouds. In addition, to efficiently learn from big data that is obtained continually, the ability to learn over time without destroying things that were learned previously is also required.

A research group led by Associate Professor Naoki Masuyama and Professor Yusuke Nojima of the Osaka Metropolitan University Graduate School of Informatics, has developed a new method that combines classification performance for data with multiple labels, with the ability to continually learn with data. Numerical experiments on real-world multi-label datasets showed that the proposed method outperforms conventional methods.