A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs

Electronic nose (E-nose), as a device intended to detect odors or flavors, has been widely used in many fields. Many labeled samples are needed to gain an ideal E-nose classification model. However, the labeled samples are not easy to obtain and there are some cases where the gas samples in the real world are complex and unlabeled. As a result, it is necessary to make an E-nose that cannot only classify unlabeled samples, but also use these samples to modify its classification model. In this paper, we first introduce a semi-supervised learning algorithm called S4VMs and improve its use within a multi-classification algorithm to classify the samples for an E-nose. Then, we enhance its performance by adding the unlabeled samples that it has classified to modify its model and by using an optimization algorithm called quantum-behaved particle swarm optimization (QPSO) to find the optimal parameters for classification. The results of comparing this with other semi-supervised learning algorithms show that our multi-classification algorithm performs well in the classification system of an E-nose after learning from unlabeled samples.

[1]  Rajib Bandyopadhyay,et al.  Application of electronic nose for industrial odors and gaseous emissions measurement and monitoring--An overview. , 2015, Talanta.

[2]  Sihao Zheng,et al.  Geoherbalism evaluation of Radix Angelica sinensis based on electronic nose. , 2015, Journal of pharmaceutical and biomedical analysis.

[3]  J E Haugen,et al.  Electronic nose and artificial neural network. , 1998, Meat science.

[4]  S. Sathiya Keerthi,et al.  Which Is the Best Multiclass SVM Method? An Empirical Study , 2005, Multiple Classifier Systems.

[5]  Ganesh Kumar Mani,et al.  Electronic noses for food quality : a review , 2015 .

[6]  Shu Fan,et al.  A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization , 2014 .

[7]  Sourav Mondal,et al.  Features extraction from electronic nose employing genetic algorithm for black tea quality estimation , 2013, 2013 International Conference on Advanced Electronic Systems (ICAES).

[8]  Frank Stam,et al.  Packaging effects of a novel explosion-proof gas sensor , 2003 .

[9]  D. Angluin,et al.  Learning From Noisy Examples , 1988, Machine Learning.

[10]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

[11]  Zhi-Hua Zhou,et al.  Towards Making Unlabeled Data Never Hurt , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[13]  Jun Wang,et al.  Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals , 2009 .

[14]  Jing Zhang,et al.  Impedance sensing and molecular modeling of an olfactory biosensor based on chemosensory proteins of honeybee. , 2013, Biosensors & bioelectronics.

[15]  Jiawei Han,et al.  Spectral regression: a unified subspace learning framework for content-based image retrieval , 2007, ACM Multimedia.

[16]  I ScottKirkpatrick.  Optimization by Simulated Annealing: Quantitative Studies , 1984 .

[17]  S. Dreyfus,et al.  Thermodynamical Approach to the Traveling Salesman Problem : An Efficient Simulation Algorithm , 2004 .

[18]  S. Adeloju,et al.  Polypyrrole-based electronic noses for environmental and industrial analysis , 2005 .

[19]  Yan Zhou,et al.  Enhancing Supervised Learning with Unlabeled Data , 2000, ICML.

[20]  Michael R. Lyu,et al.  Multi-task Learning for one-class classification , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[21]  Evor L. Hines,et al.  Enhancing electronic nose performance by sensor selection using a new integer-based genetic algorithm approach , 2005 .

[22]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[23]  Jiewen Zhao,et al.  Nondestructively sensing of total viable count (TVC) in chicken using an artificial olfaction system based colorimetric sensor array , 2016 .

[24]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[25]  Yue Shen,et al.  A PSO-SVM Method for Parameters and Sensor Array Optimization in Wound Infection Detection based on Electronic Nose , 2012, J. Comput..

[26]  Xiaojin Zhu,et al.  Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[27]  L. Zeller,et al.  Implementation of an electronic nose for continuous odour monitoring in a poultry shed , 2008 .

[28]  E. Gobbi,et al.  Rapid diagnosis of Enterobacteriaceae in vegetable soups by a metal oxide sensor based electronic nose , 2015 .

[29]  Dinggang Shen,et al.  Learning Discriminative Bayesian Networks from High-Dimensional Continuous Neuroimaging Data , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Scott Kirkpatrick,et al.  Optimization by simulated annealing: Quantitative studies , 1984 .

[31]  Daniel Cicerone,et al.  The use of an electronic nose to characterize emissions from a highly polluted river , 2008 .

[32]  Deborah H Yates,et al.  A breath test for malignant mesothelioma using an electronic nose , 2011, European Respiratory Journal.

[33]  Jiewen Zhao,et al.  Intelligent evaluation of total volatile basic nitrogen (TVB-N) content in chicken meat by an improved multiple level data fusion model , 2017 .

[34]  Wan Jun Yu,et al.  A Model of Classification for E-Nose Based on Genetic Algorithm , 2013 .

[35]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[36]  Michael R. Lyu,et al.  Efficient online learning for multitask feature selection , 2013, TKDD.

[37]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[38]  Shu Fan,et al.  Feature extraction of wound infection data for electronic nose based on a novel weighted KPCA , 2014 .

[39]  Adrian D. C. Chan,et al.  Using a metal oxide sensor (MOS)-based electronic nose for discrimination of bacteria based on individual colonies in suspension , 2011 .

[40]  Rajeshuni Ramesham,et al.  Electronic nose for space program applications. , 2003, Sensors and actuators. B, Chemical.

[41]  Yue Shen,et al.  Classification of Electronic Nose Data in Wound Infection Detection Based on PSO-SVM Combined with Wavelet Transform , 2012, Intell. Autom. Soft Comput..

[42]  Patrycja Ciosek,et al.  The analysis of sensor array data with various pattern recognition techniques , 2006 .

[43]  Shukai Duan,et al.  An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor , 2015, Inf..

[44]  Nello Cristianini,et al.  Large Margin DAGs for Multiclass Classification , 1999, NIPS.

[45]  Zhi-Hua Zhou,et al.  Semi-supervised learning using label mean , 2009, ICML '09.

[46]  Lei Zhang,et al.  Standardization of metal oxide sensor array using artificial neural networks through experimental design , 2013 .

[47]  Hui Guohua,et al.  Study of grass carp (Ctenopharyngodon idellus) quality predictive model based on electronic nose , 2012 .

[48]  Yung Kwon Sung,et al.  Portable electronic nose system with gas sensor array and artificial neural network , 2000 .

[49]  Shukai Duan,et al.  A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training , 2016, Sensors.

[50]  Greg Schohn,et al.  Less is More: Active Learning with Support Vector Machines , 2000, ICML.

[51]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[52]  Tingwen Huang,et al.  Hybrid feature matrix construction and feature selection optimization-based multi-objective QPSO for electronic nose in wound infection detection , 2016 .

[53]  Jiawei Han,et al.  Semi-Supervised Regression using Spectral Techniques , 2006 .

[54]  Bruce Hajek,et al.  A tutorial survey of theory and applications of simulated annealing , 1985, 1985 24th IEEE Conference on Decision and Control.

[55]  Zhi-Hua Zhou,et al.  Semi-supervised learning by disagreement , 2010, Knowledge and Information Systems.

[56]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[57]  Lei Zhang,et al.  Chaos based neural network optimization for concentration estimation of indoor air contaminants by an electronic nose , 2013 .

[58]  S. Sathiya Keerthi,et al.  Deterministic annealing for semi-supervised kernel machines , 2006, ICML.

[59]  M. Santonico,et al.  Detection and identification of cancers by the electronic nose. , 2012, Expert opinion on medical diagnostics.