Electronic nose sensor array optimization algorithm based on Mutal Information Feature Selection

TAO Yang; LIU Xiangyu; LIANG Zhifang

Chinese

您当前的位置：

首页 >

文章列表页 >

Electronic nose sensor array optimization algorithm based on Mutal Information Feature Selection

更新时间：2026-09-04

- Electronic nose sensor array optimization algorithm based on Mutal Information Feature Selection
- Vol. 38, Issue 7, Pages: 36-41(2021)
- 作者机构：
  
  重庆邮电大学机构通信与信息工程学院,重庆,400065
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2021
- 稿件说明：
移动端阅览
陶洋, 刘翔宇, 梁志芳. 基于互信息特征选择的电子鼻传感器阵列优化算法[J]. 微电子学与计算机, 2021, 38(7): 36-41.

TAO Yang, LIU Xiangyu, LIANG Zhifang. Electronic nose sensor array optimization algorithm based on Mutal Information Feature Selection[J]. 2021, 38(7): 36-41.
陶洋, 刘翔宇, 梁志芳. 基于互信息特征选择的电子鼻传感器阵列优化算法[J]. 微电子学与计算机, 2021, 38(7): 36-41. DOI：

TAO Yang, LIU Xiangyu, LIANG Zhifang. Electronic nose sensor array optimization algorithm based on Mutal Information Feature Selection[J]. 2021, 38(7): 36-41. DOI：

摘要

电子鼻传感器阵列目前面临传感器数量过多、数据维数过高等问题，导致电子鼻系统体积臃肿、模式识别算法精度差.基于互信息的特征选择算法筛选出电子鼻传感阵列中传感器的最优子集，可以在降低电子鼻系统体积的同时，获得更好的模式识别精度.目前传统的互信息特征选择算法没有考虑到气体与传感器的特性，应用于电子鼻系统获取的特征子集识别精度较低.针对上述问题，提出一种基于电子鼻系统性能权重的互信息特征选择算法

该算法能够衡量特征的区分性、冗余性和敏感性并筛选出更适合电子鼻阵列系统的传感器子集.将提出的方法应用于流量调制传感器阵列数据集和伤口细菌电子鼻数据集中进行传感器筛选，获得的特征子集在相同的模式识别算法中达到了更高的精度.

Abstract

Electronic nose sensor arrays are currently facing the problems of too many sensors and high data dimensions

which leads to bloated electronic nose systems and poor accuracy of pattern recognition algorithms. By selecting the optimal subset of sensors in the electronic nose sensing array through a feature selection algorithm based on mutual information

it is possible to reduce the volume of the electronic nose system and obtain better pattern recognition accuracy. At present

the traditional mutual information feature selection algorithm does not consider the characteristics of the gas and the sensor

and the recognition accuracy of the feature subset obtained by the electronic nose system is low. In this paper

a mutual information feature selection algorithm based on the performance weight of the electronic nose system is proposed. This algorithm can measure the features of distinguishability

redundancy and sensitivity to filter out a subset of sensors that are more suitable for the electronic nose array system. The method proposed is applied to the flow modulation sensor array data set and wound bacteria electronic nose data set for sensor screening

and the obtained feature subset achieves higher accuracy in the same pattern recognition algorithm.

关键词

Keywords

references

GAO W F, HU L, ZHANG P. Feature redundancy term variation for mutual information-based feature selection[J]. Applied Intelligence, 2020(50): 1272-1288. DOI:10.1007/s10489-019-01597-z.

BATTITI R. Using mutual information for selecting features in supervised neural net learning[J]. IEEE Transactions on Neural Networks, 1994, 5(4): 537-550. DOI: 10.1109/72.298224.

GAO W F, HU L, ZHANG P, et al. Feature selection considering the composition of feature relevancy[J]. Pattern Recognition Letters, 2018(112): 70-74. DOI: 10.1016/j.patrec.2018.06.005.

SUN H, TIAN F C, LIANG Z F, et al. Sensor array optimization of electronic nose for detection of bacteria in wound infection[J]. IEEE Transactions on Industrial Electronics, 2017, 64(9): 7350-7358. DOI: 10.1109/TIE.2017.2694353.

ZIYATDINOV A, FONOLLOSA J, FERNáNDEZ L, et al. Bioinspired early detection through gas flow modulation in chemo-sensory systems[J]. Sensors and Actuators B: Chemical, 2015(206): 538-547. DOI: 10.1016/j.snb.2014.09.001.

YANG H H, MOODY J. Data visualization and feature selection: new algorithms for nongaussian data[C]//Proceedings of the 12th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 1999: 687-693.

PENG H C, LONG F H, DING C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238. DOI: 10.1109/TPAMI.2005.159.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Lightweight countermeasures against Spectre attacks on the branch prediction unit

A feature-oriented context understanding and slice analysis network for tuberculosis detection

Microservices anomaly detection based on the correlation of monitoring metrics

Monte carlo analysis of V-layout trench SiC neutron detector

Simulation on the DC characteristics of double heterojunction p-GaN HEMT with AlN/GaN hybrid buffer layer

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰