Continual learning classification method with new labeled data based on the artificial immune system

In this paper, a new supervised learning classification method, continual learning classification method with new labeled data based on the artificial immune system (CLCMNLD), is proposed as a new way to improve the classification performance in real-time by continually learning the new labeled data during the testing stage. It is inspired by the mechanism that vaccines can enhance immunity. New types of memory cells were continuously cultured by learning new labeled data during the testing stage. CLCMNLD will degenerate into a common supervised learning classification method when there is no new labeled data comes out during the testing stage. The effectiveness of the proposed CLCMNLD is tested on twenty well-known datasets from the UCI Machine Learning Repository that are commonly used in the domain of data classification. The experiments reveal that CLCMNLD has better classification performance when it degenerates into a common supervised learning classification method, and it outperforms the other methods when there are some new labeled data comes out during the testing stage. The more types of new labeled data, the more advantages it has. (C) 2020 Elsevier B.V. All rights reserved.