Classification pattern recognition methods

We present in this paper an invariant pattern recognition method, applied to radiographic images of welded joints for the extraction of feature vectors of the weld defects and their classification so that they will be recognized automatically by the inspection system. 

There are many types of pattern recognition which essentially differ in the way they define classification rules. In this section, we will describe some of the approaches, which we will then develop further in the following sections. We will not try to develop a classification of pattern recognition methods but merely indicate some characteristics of the methods, that are found most often in the chemometric literature and some differences between those methods. 

Yeh and Spiegelman [24], Very good results were also obtained by using simple neural networks of the type described in Section 33.2.9 to derive a decision rule at each branching of the tree [25]. Classification trees have been used relatively rarely in chemometrics, but it seems that in general [26] their performance is comparable to that of the best pattern recognition methods. 

Borszeki J, Kepes J, Koltay L, Sarudi I (1986a) Classification of paprika quality using pattern recognition methods based on elemental composition. Acta Alimentaria 15 93... 

A great variety of different methods for multivariate classification (pattern recognition) is available (Table 5.6). The conceptually most simply one is fc-NN classification (Section 5.3.3), which is solely based on the fundamental hypothesis of multivariate data analysis, that the distance between objects is related to the similarity of the objects. fc-NN does not assume any model of the object groups, is nonlinear, applicable to multicategory classification, and mathematically very simple furthermore, the method is very similar to spectral similarity search. On the other hand, an example for a rather sophisticated classification method is the SVM (Section 5.6). 

Figure 14.2 Pattern recognition methods classification used in pharmaceutical identification. Shaded blocks refer to the more commonly used techniques.

In order to characterise wine samples into the mentioned four classes, a supervised pattern recognition method (LDA) was applied. The results obtained gave 100% correct classification for the three classes (Barbera Oltrepo, Barbera Piemonte and Barbera Alba) and only one Barbera Asti sample was not correctly classified (cross-validation error rate 1.89%). 

The answers to these questions will usually be given by so-called unsupervised learning or unsupervised pattern recognition methods. These methods may also be called grouping methods or automatic classification methods because they search for classes of similar objects (see cluster analysis) or classes of similar features (see correlation analysis, principal components analysis, factor analysis). 

Questions of type (2.1) may be answered by analysis of variance or by discriminant analysis. All these methods may be found under the name supervised learning or supervised pattern recognition methods. In the sense of question (2.1.3) one may speak of supervised classification or even better of re-classification methods. In situations of type (2.2) methods from the large family of regression methods are appropriate. 

Gartland KP, Beddell CR, Lindon JC, Nicholson JK. Application of pattern recognition methods to the analysis and classification of toxicological data derived from proton nuclear magnetic resonance spectroscopy of urine. Mol Pharmacol 1991 39 629 642. 

In this chapter, the three major subdivisions of pattern-recognition methodology are discussed (1) mapping and display, (2) clustering, and (3) classification. The procedures that must be implemented to apply pattern-recognition methods are also enumerated. Specific emphasis is placed on the application of these techniques to problems in biological and environmental analyses. 

Other attempts have focused on proof-of-concept aspects of classification, using standard supervised pattern recognition methods. Major deficiencies exist in this approach when attempting the classification of biomedical spectra, including the following. 

Gartland, K.P.R. Beddell, C.R. Lindon, J.C. Nicholson, J.K. Application of Pattern-Recognition Methods to the Analysis and Classification of Toxicological Data Derived from Proton Nuclear-Magnetic-Resonance Spectroscopy of Urine. Mol. Pharmacol. 39(5), 629-642 (1991). 

Hagberg, G. From Magnetic Resonance Spectroscopy to Classification of Tumors. A Review of Pattern Recognition Methods, NMR Biomed. 11(4-5), 148— 156 (1998). 

To establish a correlation between the concentrations of different kinds of nucleosides in a complex metabolic system and normal or abnormal states of human bodies, computer-aided pattern recognition methods are required (15, 16). Different kinds of pattern recognition methods based on multivariate data analysis such as principal component analysis (PCA) (8), partial least squares (16), stepwise discriminant analysis, and canonical discriminant analysis (10, 11) have been reported. Linear discriminant analysis (17, 18) and cluster analysis were also investigated (19,20). Artificial neural network (ANN) is a branch of chemometrics that resolves regression or classification problems. The applications of ANN in separation science and chemistry have been reported widely (21-23). For pattern recognition analysis in clinical study, ANN was also proven to be a promising method (8). 

The aim of supervised classification is to create rules based on a set of training samples belonging to a priori known classes. Then the resulting rules are used to classify new samples in none, one, or several of the classes. Supervised pattern recognition methods can be classified as parametric or nonparametric and linear or nonlinear. The term parametric means that the method makes an assumption about the distribution of the data, for instance, a Gaussian distribution. Frequently used parametric methods are EDA, QDA, PLSDA, and SIMCA. On the contrary, kNN and CART make no assumption about the distribution of the data, so these procedures are considered as nonparametric. Another distinction between the classification techniques concerns the... 

Fig. 22.3 The chemometric analysis of multivariate data tables. Two major types of studies can be defined (1) correlation between biological and (physico)chemical data using regression techniques and (2) classification of compounds or descriptors using pattern recognition methods.

The classification methods discussed in the previous section are all based on statistical tests wliich require normal data distribution. If this condition is not fulfilled the so-called non-probabihstic , non-parametric or heuristic classification techniques must be used. These techniques are also frequently referred to as pattern recognition methods. They are based on geometrical and not on statistical considerations, starting from a representation of the compounds... 

Gemperline, P.J. Boyer, N.R. (1995). Classification of near-infrared spectra using wavelength distances Comparisons to the Mahalanobis distance and Residual Variance methods. Analytical Chemistry. Vol.67, pp. 160-166. ISSN 0003-2700 Gonzalez, A.G. (2007). Use and misuse of supervised pattern recognition methods for interpreting compositional data. Journal of Chromatograpy A. Vol. 1158, pp. 215-225. ISSN 0021-9673... 

The first analytical application of a pattern recognition method dates back to 1969 when classification of mass spectra with respect to certain molecular mass classes was tried with the LLM. The basis for classification with the LLM is a discriminant function that divides the -dimensional space into category regions that can be further used to predict the category membership of a test sample. 

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