Paper Info
Title
Continual learning classification method with the weighted k-nearest neighbor rule for time-varying data space based on the artificial immune system
Abstract
Supervised learning classification methods play an important role in various fields. However, most of them cannot effectively classify the data from time-varying data spaces, for they lack continual learning ability. Inspired by the intelligent mechanism that memory cells of the biological immune system can evolve with the evolution of invaders, a continual learning classification method with the weighted k-nearest neighbor rule for time-varying data space (WKNN-CLCMTVD) is proposed. Memory cells selected by the weighted k-nearest neighbor rule are used to identify the type of testing data. Memory cells are continuously cultivated, updated, and eliminated through learning testing data to improve the classification ability of WKNN-CLCMTVD. It degenerates into a standard supervised learning classification method when all data independent of time. Take experiments on twenty benchmark datasets, a 2-dimensional synthetic dataset, and XJTU-SY rolling element bearing accelerated life test datasets to evaluate the performance of WKNN-CLCMTVD. Results show that it has better classification ability for time-invariant data and outperforms the other methods for time-varying data space. Results also show that its running speed is far faster than that of other continual learning classification methods.
Year
DOI
Venue
2022
10.1016/j.knosys.2022.108145
Knowledge-Based Systems
Keywords
DocType
Volume
Artificial immune system,Time-varying data,Classification,Continual learning,Weighted k-nearest neighbor
Journal
240
ISSN
Citations 
PageRank 
0950-7051
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Dong Li111548.55
Ming Gu255474.82
Shulin Liu310.69
Sun Xin410629.38
Lanlan Gong500.34
Kun Qian600.34