Sensors and Materials

Young Researcher Paper Award 2023
🥇Winners

Notice of retraction
Vol. 34, No. 8(3), S&M3042

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.

Tweets by Journal_SandM Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English 日本語

Instructions for manuscript preparation
English 日本語

Template
English

Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.

MYU Research
(proofreading and recording)

MYU K.K.
(translation service)

The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)

Sensors and Materials, Volume 25, Number 8 (2013)
Copyright(C) MYU K.K.

pp. 527-538
S&M946 Research Paper
https://doi.org/10.18494/SAM.2013.839
Published: October 28, 2013

Feature Selection Using Support Vector Machines and Independent Component Analysis for Wound Infection Detection by Electronic Nose [PDF]

Jingwei Feng, Fengchun Tian, Pengfei Jia, Qinghua He, Yue Shen and Tao Liu

(Received June 13, 2012; Accepted January 21, 2013)

Keywords: feature selection, electronic nose, support vector machine, independent component analysis, wound infection detection

When mice are used as experimental subjects in the detection of wound infection based on electronic nose (Enose), the background, i.e., the smell of the mice themselves, is very strong, and most useful information is buried in it. A new feature selection technique specifically designed to work with support vector machine (SVM) and independent component analysis (ICA) is introduced. The features that represent background and noise are eliminated to improve classification accuracy. To assess this new method, two other datasets are used as validation, and four other feature selection methods are compared. The result shows that this method is effective and practical for feature selection in the detection of wound infection. Besides, this method is also useful in dimensionality reduction.

Corresponding author: Jingwei Feng

Cite this article
Jingwei Feng, Fengchun Tian, Pengfei Jia, Qinghua He, Yue Shen and Tao Liu, Feature Selection Using Support Vector Machines and Independent Component Analysis for Wound Infection Detection by Electronic Nose, Sens. Mater., Vol. 25, No. 8, 2013, p. 527-538.