Design of lightweight incremental ensemble learning algorithm

被引：0

作者：

Ding J. ^{[1
]}

Tang J. ^{[1
]}

Yu Z. ^{[1
]}

机构：

[1] School of Electronic Engineering, Xidian University, Xi'an

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 04期

关键词：

Classification and regression tree (CART); Computational complexity; Emitter classification; Ensemble learning; Open set recognition;

D O I：

10.12305/j.issn.1001-506X.2021.04.01

中图分类号：

学科分类号：

摘要：

Conventional classification and regression tree (CART) can only increase the cognition of new categories by retraining the entire model, causing a great increase in training costs when the number of sample categories is large. To solve this problem, a lightweight incremental ensemble learning algorithm is proposed. When new categories enter the training set, we can classify those new categories by only adding CART base classifiers with the ability of open set recognition into the original ensemble learning algorithm. No retraining is required, so the computational complexity is reduced and the learning process is simplified. In the simulation experiments with the background of emitter classification, the results show that this algorithm can maintain the classification accuracy of more than 90% when the signal noise ratio equal to or larger than -4 dB. In the case of a large number of categories to be classified, this algorithm can significantly reduce the training cost compared with conventional CART. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：861 / 867

页数：6

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