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

doi:10.19682/j.cnki.1005-8885.2024.1003

中国邮电高校学报(英文) ›› 2024, Vol. 31 ›› Issue (3): 15-29.doi: 10.19682/j.cnki.1005-8885.2024.1003

• Artificial Intelligence • 上一篇下一篇

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

赵广元,雷渝

School of Automation, Xi'an University of Posts and Telecommunications, Xi'an 710000, China

收稿日期:2022-06-22 修回日期:2023-03-14 出版日期:2024-06-30 发布日期:2024-06-30
通讯作者: 雷渝 E-mail:15829777573@163.com

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

Zhao Guangyuan, Lei Yu

School of Automation, Xi'an University of Posts and Telecommunications, Xi'an 710000, China

Received:2022-06-22 Revised:2023-03-14 Online:2024-06-30 Published:2024-06-30
Contact: Lei Yu E-mail:15829777573@163.com

摘要/Abstract

摘要： 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.

关键词: deep kernel extreme learning machine(DKELM), improved sparrow search algorithm(ISSA), classifier, parameters optimization

Abstract: 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.

Key words: deep kernel extreme learning machine(DKELM), improved sparrow search algorithm(ISSA), classifier, parameters optimization

Zhao Guangyuan, Lei Yu. Deep kernel extreme learning machine classifier based on the improved sparrow search algorithm[J]. The Journal of China Universities of Posts and Telecommunications, 2024, 31(3): 15-29.

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

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

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