This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Sensors and imaging for wound healing: a review.

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing.

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Signal and Data Processing for Machine Olfaction and Chemical Sensing: A Review

The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data To be able to learn from data, the dimensionality of the data should be reduced first Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories We propose a unified SI framework and use it to explain different approaches to FS Different methods, techniques, and their settings are explained, which have been used for various FS aspects The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem

/pdf/swarm-intelligence-algorithms-for-feature-selection-a-review-148v6fova7.pdf

Swarm Intelligence Algorithms for Feature Selection: A Review

Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature extraction methods and pattern recognition method selection. For a specific application, the feature extraction method is a basic part of these three optimizations and a key point in E-nose system performance improvement. The aim of a feature extraction method is to extract robust information from the sensor response with less redundancy to ensure the effectiveness of the subsequent pattern recognition algorithm. Many kinds of feature extraction methods have been used in E-nose applications, such as extraction from the original response curves, curve fitting parameters, transform domains, phase space (PS) and dynamic moments (DM), parallel factor analysis (PARAFAC), energy vector (EV), power density spectrum (PSD), window time slicing (WTS) and moving window time slicing (MWTS), moving window function capture (MWFC), etc. The object of this review is to provide a summary of the various feature extraction methods used in E-noses in recent years, as well as to give some suggestions and new inspiration to propose more effective feature extraction methods for the development of E-nose technology.

https://www.mdpi.com/1424-8220/15/11/27804/pdf

Electronic Nose Feature Extraction Methods: A Review.

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 method is proposed to eliminate the background and discriminate wound infection based on a gas sensor array composed of 15 gas sensors. It employs thirteen-scale and the first order Daubechies (db1) wavelet analysis to decompose each signal of the sensor array. Direct multiplication of wavelet transform coefficients at corresponding scales between response signals of the wounded and healthy mice are used to eliminate background smell. The approximation coefficients of which the background had been eliminated are used as the inputs of RBF (Radical Basis Function) network for discrimination. The result shows that this method is effective and practical for background elimination in the detection of wound infection. Besides, this method is also useful in dimensionality reduction.

A background elimination method based on wavelet transform in wound infection detection by electronic nose

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

Purpose – The purpose of this paper is to improve the performance of E-nose in the detection of wound infection. Feature extraction and selection methods have a strong impact on the performance of pattern classification of electronic nose (E-nose). A new hybrid feature matrix construction method and multi-objective binary quantum-behaved particle swarm optimization (BQPSO) have been proposed for feature extraction and selection of sensor array. Design/methodology/approach – A hybrid feature matrix constructed by maximum value and wavelet coefficients is proposed to realize feature extraction. Multi-objective BQPSO whose fitness function contains classification accuracy and a number of selected sensors is used for feature selection. Quantum-behaved particle swarm optimization (QPSO) is used for synchronization optimization of selected features and parameter of classifier. Radical basis function (RBF) network is used for classification. Findings – E-nose obtains the highest classification accuracy when the ...

Hybrid feature matrix construction and feature selection optimization-based multi-objective QPSO for electronic nose in wound infection detection

Three feature extraction methods, extraction from original response curves of sensors,�curvefittingparametersandtransformdomains,�forpathogendetectionbased� onanelectronicnosehavebeendiscussed.� � Byusingtheintegrals,� coefficientsof� exponentialfittingwithtwoparametersandhyperbolictangentfunctionfitting,� Fourier� coefficients,� andwaveletcoefficientsasfeatures,�100%�identificationaccuracywiththe� radicalbasisfunctionneuralnetwork� (RBFNN)� classifierwasreachedfortheseven� pathogens. Theoretical analysis and experimental results indicate that the methods based on dynamic response are better than those based on steady response and can provide accurateidentificationofcommonpathogenspresentinwoundinfection.

Jia Yan

Papers

Electronic Nose Feature Extraction Methods: A Review.

A background elimination method based on wavelet transform in wound infection detection by electronic nose

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

Hybrid feature matrix construction and feature selection optimization-based multi-objective QPSO for electronic nose in wound infection detection

Feature Extraction from Sensor Data for Detection of Wound Pathogen Based on Electronic Nose