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

ISSN (print) 0914-4935
ISSN (online) 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.
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.



Forthcoming Regular Issues


Forthcoming Special Issues

Special Issue on Sensors and Materials: Emerging Investigators in Japan
Guest editor, Tsuyoshi Minami (The University of Tokyo)


Special issue on Novel Materials and Sensing Technologies on Electronic and Mechanical Devices (2)-1
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Hsien-Wei Tseng (Longyan University)


Special Issue on Materials, Devices, Circuits, and Analytical Methods for Various Sensors (4)
Guest editor, Chien-Jung Huang (National University of Kaohsiung), Cheng-Hsing Hsu (National United University), Ja-Hao Chen (Feng Chia University), and Wei-Ling Hsu (Huaiyin Normal University)
Conference website


Special issue on Novel Materials and Sensing Technologies on Electronic and Mechanical Devices (2)-2
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Hsien-Wei Tseng (Longyan University)


Special Issue on New Trends in Robots and Their Applications
Guest editor, Ikuo Yamamoto (Nagasaki University)


Special Issue on Artificial Intelligence in Sensing Technologies and Systems
Guest editor, Prof. Lin Lin (Dalian University of Technology)
Call for paper


Copyright(C) MYU K.K. All Rights Reserved.