An Enhanced Gas Sensor Data Classification Method Using Principal Component Analysis and Synthetic Minority Over-Sampling Technique Algorithms

This study addresses the challenge of multi-dimensional and small gas sensor data classification using a gelatin–carbon black (CB-GE) composite film sensor, achieving 91.7% accuracy in differentiating gas types (ethanol, acetone, and air). Key techniques include Principal Component Analysis (PCA) fo...

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Bibliographic Details
Main Authors:	Xianzhang Zeng, Muhammad Shahzeb, Xin Cheng, Qiang Shen, Hongyang Xiao, Cao Xia, Yuanlin Xia, Yubo Huang, Jingfei Xu, Zhuqing Wang
Format:	Article
Language:	English
Published:	MDPI AG 2024-12-01
Series:	Micromachines
Subjects:	machine learning gas sensor PCA SMOTE KNN
Online Access:	https://www.mdpi.com/2072-666X/15/12/1501
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An Enhanced Gas Sensor Data Classification Method Using Principal Component Analysis and Synthetic Minority Over-Sampling Technique Algorithms

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