Deep kernel extreme learning machine classifier based on the improved sparrow search algorithm

被引：0
|
作者：
Guangyuan, Zhao [1 ]
Yu, Lei [1 ]
机构：
[1] School of Automation, Xian University of Posts and Telecommunications, Xian,710000, China
来源：
Journal of China Universities of Posts and Telecommunications | 2024年 / 31卷 / 03期
关键词：
In the classification problem; deep kernel extreme learning machine (DKELM) has the characteristics of efficient processing and superior performance; but its parameters optimization is difficult. To improve the classification accuracy of DKELM; a DKELM algorithm optimized by the improved sparrow search algorithm (ISSA); named as ISSA DKELM; is proposed in this paper. Aiming at the parameter selection problem of DKELM; the DKELM classifier is constructed by using the optimal parameters obtained by ISSA optimization. In order to make up for the shortcomings of the basic sparrow search algorithm (SSA); the chaotic transformation is first applied to initialize the sparrow position. Then; the position of the discoverer sparrow population is dynamically adjusted. A learning operator in the teaching learning based algorithm is fused to improve the position update operation of the joiners. Finally; the Gaussian mutation strategy is added in the later iteration of the algorithm to make the sparrow jump out of local optimum. The experimental results show that the proposed DKELM classifier is feasible and effective; and compared with other classification algorithms; the proposed DKELM algorithm aciheves better test accuracy. © 2024; Beijing University of Posts and Telecommunications. All rights reserved;
D O I：
10.19682/j.cnki.1005-8885.2024.1003
中图分类号：
学科分类号：
摘要：
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页码：15 / 29
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