Paper Info
Title
Continual learning classification method with single-label memory cells based on the intelligent mechanism of the biological immune system
Abstract
The traditional batch learning classification methods need to obtain all kinds of data once before training. This makes them unable to recognize the data from the unseen types and cannot continuously enhance their classification ability through learning the testing data in the testing process, because they lack continual learning ability. Inspired by the continual learning mechanism of the biological immune system (BIS), this paper proposed a continual learning classification method with single-label memory cells (S-CLCM). The type of testing data is identified by memory cells, and the data type from unseen types is determined by an affinity threshold. New memory cells are cultivated continuously by learning the testing data to enhance the classification ability of S-CLCM gradually. Every memory cell has the same size and a unique type. It becomes a standard batch learning classification method or a standard clustering method under certain conditions. Take the experiments on twenty benchmark datasets to estimate its classification performance and possible superiority. Results show S-CLCM has good performance when it becomes a standard batch learning classification method, and S-CLCM is superior to the other classical classification algorithms when the data from unseen types or new labeled data appear during the testing process. It can improve the classification accuracy by up to 33%, and by at least 14%.
Year
DOI
Venue
2022
10.3233/JIFS-212226
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
DocType
Volume
Classification, continual learning, biological immune system, machine learning, artificial immune algorithm
Journal
42
Issue
ISSN
Citations 
4
1064-1246
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Dong Li111548.55
Lanlan Gong200.34
Shulin Liu300.68
Xin Sun400.68
Ming Gu555474.82
Kun Qian600.34