Continual learning classification method with constant-sized memory cells based on the artificial immune system

Abstract

Most classification methods cannot further improve their classification performance by learning the testing data during the testing stage, for lacking continual learning ability. A new classification method, continual learning classification method with constant-sized memory cells based on the artificial immune system (C-CLCM), is proposed. It is inspired by the continual learning mechanism of the biological immune system. C-CLCM gradually enhances its classification performance by continually learning the testing data especially the new types of labeled data and new types of unlabeled data during the testing stage. At the same moment, it updates the existing memory cells and culture new types of memory cells. C-CLCM degenerates into a common supervised learning classification method under certain conditions. To assess its performance and possible advantages, the experiments on well-known datasets from the UCI repository were performed. Results show that C-CLCM has better classification performance when it degenerates into a common supervised learning classification method. It outperforms the other methods when the training data do not cover all types. The less type of training, the more advantages it has.