基于互信息特征选择的电子鼻传感器阵列优化算法

陶洋; 刘翔宇; 梁志芳

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基于互信息特征选择的电子鼻传感器阵列优化算法

更新时间：2026-09-04

- 基于互信息特征选择的电子鼻传感器阵列优化算法
- Electronic nose sensor array optimization algorithm based on Mutal Information Feature Selection
- 微电子学与计算机 2021年38卷第7期页码：36-41
- 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：
  
  国家重点研发计划资助 2019YFB2102001重庆市基础研究与前沿探索项目 cstc2018jcyjAX0549重庆市教育委员会科学技术研究项目 KJQN201800617
- DOI：
  中图分类号：
- 纸质出版：2021
- 稿件说明：
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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.

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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.

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