2012
DOI: 10.3390/s120302818
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Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

Abstract: Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis… Show more

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Cited by 32 publications
(21 citation statements)
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“…It minimized the differences among points in the same sample group while maximizing the differences among the points in different sample groups to generate high discriminate efficiency (Fu et al . ).…”
Section: Methodsmentioning
confidence: 99%
“…It minimized the differences among points in the same sample group while maximizing the differences among the points in different sample groups to generate high discriminate efficiency (Fu et al . ).…”
Section: Methodsmentioning
confidence: 99%
“…Among the dimensionality reduction and classification methods, the most popular ones include Principal Component Analysis (PCA) [164], Linear Discriminant Analysis (LDA) [165], Quadratic Discriminant Analysis (QDA) [166], Support Vector Machine (SVM) [167], Cluster Analysis (CA) [168], Factorial Discriminant Analysis (FDA) [169], Canonical Discriminant Analysis (CDA) [170], Hierarchical Clustering (HC) [171], and Artificial Neural Network (ANN) [120,164,172]. In turn, the concentration of samples is usually determined with Partial Least Squares (PLS) [173], Multiple Linear Regression (MLR) [174], Ridge Regression (RR), or regression ANN.…”
Section: Analysis Of Received Signalsmentioning
confidence: 99%
“…In turn, the concentration of samples is usually determined with Partial Least Squares (PLS) [173], Multiple Linear Regression (MLR) [174], Ridge Regression (RR), or regression ANN. In the scientific literature on sensor arrays, the application of PCA, both for the preliminary assessment of measurement results as well as the main method of data analysis, is very common [164,175].…”
Section: Analysis Of Received Signalsmentioning
confidence: 99%
“…Principal components analysis, considered an "algorithm in biometrics" (Karamizadeh et al, 2013), or an "exploratory tool" (Henderson, 2006), is one of the oldest and more utilized multivariate techniques over time. There are many examples that demonstrate the accuracy of information extracted from many experimental data by principal components multivariate analysis facilities: fraud detection in automobile insurance domain (Brockett et al, 2002), digital images classification (Ehsanirad and Kumar, 2010;Ostaszewski et al, 2015), missing data values identification based on probabilistic formula of a theoretical mathematical model (Ilin and Raiko, 2010;Dray and Josse, 2015), pattern classification of drugs in pharmacology (Bober et al, 2011), cancer diagnose (Bair et al, 2006), pattern analysis of wine (Camara et al, 2006;Giaccio and Vicentini, 2008;Fu et al, 2012) or green tea (Fu et al, 2012), animal behavior depending on environmental conditions (Budaev, 2010), quality evaluation of dairy products (Chapman et al, 2000), fruits classification based on qualitative parameters (Zaragoza, 2015), plants diversity (Casas and Ninot, 2003;Henderson, 2006), foliage identification of plant species based on different characteristics (Ehsanirad and Kumar, 2010;Kadir et al, 2012), genetic variability of plants germplasm (Evgenidis et al, 2011;Mahendran et al, 2015), submergence tolerance of flooded plants in river floodplains (Mommer et al, 2006), selection of the most important criteria of Triticum aestivum genotypes to improve genetically the yield of bread wheat (Beheshtizadeh et al, 2013), etc. This multivariate technique use a linear model in orthogonal projection for extractingessential observations based on amount of the data variance (Casas and Ninot, 2003;Henderson, 2006;Giaccio and Vicentini, 2008;Ilin and Raiko, 2010;Karamizadeh et al, 2013;…”
Section: Introductionmentioning
confidence: 99%