Paper Info
Title
A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections.
Abstract
An electronic nose (E-nose) consisting of 14 metal oxide gas sensors and one electronic chemical gas sensor has been constructed to identify four different classes of wound infection. However, the classification results of the E-nose are not ideal if the original feature matrix containing the maximum steady-state response value of sensors is processed by the classifier directly, so a novel pre-processing technique based on supervised locality preserving projections (SLPP) is proposed in this paper to process the original feature matrix before it is put into the classifier to improve the performance of the E-nose. SLPP is good at finding and keeping the nonlinear structure of data; furthermore, it can provide an explicit mapping expression which is unreachable by the traditional manifold learning methods. Additionally, some effective optimization methods are found by us to optimize the parameters of SLPP and the classifier. Experimental results prove that the classification accuracy of support vector machine (SVM combined with the data pre-processed by SLPP outperforms other considered methods. All results make it clear that SLPP has a better performance in processing the original feature matrix of the E-nose.
Year
DOI
Venue
2016
10.3390/s16071019
SENSORS
Keywords
Field
DocType
sensor data,electronic nose,SLPP,data pre-processing,wound infection
Electronic nose,Nonlinear structure,Data mining,Locality,Pattern recognition,Support vector machine,Data pre-processing,Artificial intelligence,Feature matrix,Engineering,Classifier (linguistics),Nonlinear dimensionality reduction
Journal
Volume
Issue
Citations 
16
7
0
PageRank 
References 
Authors
0.34
11
6
Name
Order
Citations
PageRank
Pengfei Jia1364.81
Tailai Huang271.48
Li Wang3202.53
Shukai Duan459554.03
Jia Yan5343.75
Lidan Wang637342.92