| 轻量化的增量式集成学习算法设计 |
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| 引用本文： | 丁嘉辉,汤建龙,于正洋. 轻量化的增量式集成学习算法设计[J]. 系统工程与电子技术, 2021, 43(4): 861-867. DOI: 10.12305/j.issn.1001-506X.2021.04.01 |
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| 作者姓名： | 丁嘉辉 汤建龙 于正洋 |
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| 作者单位： | 西安电子科技大学电子工程学院, 陕西 西安 710071 |
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| 基金项目： | 中央高校基本科研业务费专项资金;西安电子科技大学研究生创新基金资助课题。 |
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| 摘 要： | 常规的分类与回归树算法(classification and regression tree,CART)只能通过重新训练来增加对新类别的认知,导致样本类别数量较多时训练成本大幅增加.针对这一问题,提出一种轻量化的增量式集成学习算法:当新的类别进入到训练集中,只需在原有集成学习算法中添加具有开集识别能力的CART基分类器...
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| 关 键 词： | 分类与回归树 计算复杂度 开集识别 集成学习 辐射源分类 |
| 收稿时间： | 2020-08-10 |
Design of lightweight incremental ensemble learning algorithm |
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| DING Jiahui,TANG Jianlong,YU Zhengyang. Design of lightweight incremental ensemble learning algorithm[J]. System Engineering and Electronics, 2021, 43(4): 861-867. DOI: 10.12305/j.issn.1001-506X.2021.04.01 |
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| Authors: | DING Jiahui TANG Jianlong YU Zhengyang |
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| Affiliation: | School of Electronic Engineering, Xidian University, Xi'an 710071, China |
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| Abstract: | 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. |
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| Keywords: | classification and regression tree(CART) computational complexity open set recognition ensemble learning emitter classification |
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