A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell

Dang, Wei; Guo, Jialiang; Liu, Mingzhe; Liu, Shan; Yang, Bo; Yin, Lirong; Zheng, Wenfeng

doi:10.3390/app12189213

Open AccessArticle

A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell

by

Wei Dang

¹

,

Jialiang Guo

¹,

Mingzhe Liu

^1,2,*

,

Shan Liu

^1,*

,

Bo Yang

¹

,

Lirong Yin

³

and

Wenfeng Zheng

^1,*

¹

School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China

²

College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China

³

Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA

^*

Authors to whom correspondence should be addressed.

Appl. Sci. 2022, 12(18), 9213; https://doi.org/10.3390/app12189213

Submission received: 30 July 2022 / Revised: 5 September 2022 / Accepted: 9 September 2022 / Published: 14 September 2022

(This article belongs to the Special Issue Advances in Artificial Intelligence for Perception Augmentation and Reasoning)

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Abstract

At present, machine sense of smell has shown its important role and advantages in many scenarios. The development of machine sense of smell is inseparable from the support of corresponding data and algorithms. However, the process of olfactory data collection is relatively cumbersome, and it is more difficult to collect labeled data. However, in many scenarios, to use a small amount of labeled data to train a good-performing classifier, it is not feasible to rely only on supervised learning algorithms, but semi-supervised learning algorithms can better cope with only a small amount of labeled data and a large amount of unlabeled data. This study combines the new weighted kernel with SKELM and proposes a semi-supervised extreme learning machine algorithm based on the weighted kernel, SELMWK. The experimental results show that the proposed SELMWK algorithm has good classification performance and can solve the semi-supervised gas classification task of the same domain data well on the used dataset.

Keywords: machine sense of smell; supervised learning; semi-supervised learning; SELMWK

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MDPI and ACS Style

Dang, W.; Guo, J.; Liu, M.; Liu, S.; Yang, B.; Yin, L.; Zheng, W. A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell. Appl. Sci. 2022, 12, 9213. https://doi.org/10.3390/app12189213

AMA Style

Dang W, Guo J, Liu M, Liu S, Yang B, Yin L, Zheng W. A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell. Applied Sciences. 2022; 12(18):9213. https://doi.org/10.3390/app12189213

Chicago/Turabian Style

Dang, Wei, Jialiang Guo, Mingzhe Liu, Shan Liu, Bo Yang, Lirong Yin, and Wenfeng Zheng. 2022. "A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell" Applied Sciences 12, no. 18: 9213. https://doi.org/10.3390/app12189213

APA Style

Dang, W., Guo, J., Liu, M., Liu, S., Yang, B., Yin, L., & Zheng, W. (2022). A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell. Applied Sciences, 12(18), 9213. https://doi.org/10.3390/app12189213

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A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell

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