.png){kind=icon}
返回
Information-Theoretic Ensemble Feature Selection With Multi-Stage Aggregation for Sensor Array Optimization
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2020.3000756.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In an electronic nose (e-nose) system, the gas sensor array is the key component for detecting the volatile profile of a sample. An inappropriate sensor combination can lead to several issues, including overlapping selectivities, high computational overhead, performance degradation, etc. To deal with this problem, typically, a feature selection algorithm (FSA) is utilized to optimize the gas sensor combination in the sensor array. However, the instability of the FSA output is a serious problem in sensor array optimization. An unstable FSA output makes it difficult to conclude the general sensor combination. Hence, in this study, the Information-Theoretic Ensemble Feature Selection (ITEFS) method is proposed to deal with FSA instability. In the experiment, twelve homogeneous e-nose data sets were used that corresponded to twelve types of beef samples. Moreover, the outputs from eleven information-theoretic FSAs were aggregated to determine the general sensor combination. The performance of the selected sensors was validated based on the number of overlapping selectivities, F-measure, and execution time. The results indicated that ITEFS can more effectively reduce overlapping selectivities than other FSAs. The selected sensors also displayed comparable performance in classification tasks to other FSAs even when using fewer sensors, with an average F-measure of more than 99% using k-NN and SVM on all data sets. This indicates that the combination of selected sensors had sufficiently good generalization to detect the various types of beef samples. In addition, the utilization of a lower number of sensors also reduced the execution time in the training and testing processes.
Keyword:
Sensor arrays
Gas detectors
Feature extraction
Optimization
Sensor phenomena and characterization
Beef quality monitoring
electronic nose
ensemble feature selection
sensor array optimization
stability of feature selection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏Catalytic Pyrolysis of Microcrystalline Cellulose with Five Molecular Sieve Based Catalysts in Py-GC-MS五种分子筛基催化剂在Py-GC-MS中的微晶纤维素催化热解
OAAI
OAAI分享收藏分享收藏
查看更多内容