Autosoft Journal

Online Manuscript Access


Classification of Electronic Nose Data in Wound Infection Detection Based on PSO-SVM Combined with Wavelet Transform


Authors



Abstract

In this paper, a new method for classifying electronic nose data in rats wound infection detection based on support vector machine (SVM) and wavelet analysis was developed. Signals of the sensors were decomposed using wavelet analysis for feature extraction and a PSO-SVM classifier was developed for pattern recognition. The sensor array was optimized and model parameters were selected to achieve the maximum classification accuracy of SVM. Particle swarm optimization (PSO) was used to achieve optimization of the sensor array and the SVM model parameters. A classification rate of 97.5 was achieved by the proposed method for data discrimination. Compared with the methods of radial basis function (RBF) neural network classifier with maximum or wavelet coefficients feature and SVM without sensor array optimization, this method gave better performance on classification rate and time consumption in rats wound infection data recognition.


Keywords


Pages

Total Pages: 13
Pages: 967-979

DOI
10.1080/10798587.2012.10643302


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways


Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Published

Volume: 18
Issue: 7
Year: 2012

Cite this document


References

Daubechies, Ingrid. "Ten Lectures on Wavelets." (1992): n. pag. Crossref. Web. https://doi.org/10.1137/1.9781611970104

V. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, 1995.

F.C. Tian, S.X. Yang, K Dong, “Study on Noise Feature in Sensor Array of an Electronic Nose”, IEEE International Conference On Networking, Sensing and Control, Tucson, Arizona, U.S.A, pp. 19–22, 2005.

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

TWO YEAR CITATIONS PER DOCUMENT (SJR DATA): 0.993 (2018)
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."





Journal: 1995-Present


CONTACT INFORMATION


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048
EMAIL: tsiepress@gmail.com
WEB: http://www.wacong.org/tsi/