Pattern recognition classification

D. Scott, W. Dunn and S. Emery, Pattern recognition classification and identification of trace organic pollutants in ambient air from mass spectra. J. Res. Natl. Bur. Stand., 93 (1988) 281-283. 

Also, we do not cover several typical chemometrics types of analyses, such as cluster analysis, experimental design, pattern recognition, classification, neural networks, wavelet transforms, qualimetrics etc. This explains our decision not to include the word chemometrics in the title. 

Anthony, M.L. Sweatman, B.C. Beddell, C.R. Lindon, J.C. Nicholson, J.K. Pattern Recognition Classification of the Site of Nephrotoxicity Based on Metabolic Data Derived from High Resolution Proton Nuclear Magnetic Resonance Spectra of Urine, Mol. Pharmacol. 46, 199-211 (1994). 

Anthony ML, Sweatman BC, Beddell CR, Lindoon JC, Nicholson JK Pattern recognition classification of the site of nephrotoxicity based on metabolic data from proton nuclear magnetic resonance spectra of urine. Mol Pharmacol 1994 46 199-211. 

The name chemometrics was first used by Svante Wold in a Swedish journal. In general, it refers to a chemical discipline that focuses on maximizing the extraction of information from data and experimental measurements with the aid of mathematical, computational, and logic methods. The data or information collected are submitted for analysis by one or more methods of chemometrics typically associated with pattern recognition, classification, or prediction. 

The features of the chemometric approach can perhaps be best understood by comparing it with the classical approach. The classical approach aims to understand effects—which factors are dominant and which ones are negligible—whereas the chemometric approach gives up the necessity to understand the effects, and points out other aims such as prediction, pattern recognition, classification, etc. 

Spraul M, Neidig P, Klauck U, Kessler P, Holmes E, Nicholson JK, et al. Automatic reduction of NMR spectroscopic data for statistical and pattern-recognition classification of samples. J Pharm Biomed Anal 1994 12 1215-25. 

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. 

Often the goal of a data analysis problem requites more than simple classification of samples into known categories. It is very often desirable to have a means to detect oudiers and to derive an estimate of the level of confidence in a classification result. These ate things that go beyond sttictiy nonparametric pattern recognition procedures. Also of interest is the abiUty to empirically model each category so that it is possible to make quantitative correlations and predictions with external continuous properties. As a result, a modeling and classification method called SIMCA has been developed to provide these capabihties (29—31). 

Thus, they share exactly the same solution (H) and performance criteria (y ) spaces. Furthermore, since their role is simply to estimate y for a given X, no search procedures S are attached to classical pattern recognition techniques. Consequently, the only element that dilfers from one classification procedure to another is the particular mapping procedure / that is used to estimate y(x) and/ or ply = j x). The available set of (x, y) data records is used to build /, either through the construction of approximations to the decision boundaries that separate zones in the decision space leading to different y values (Fig. 2a), or through the construction of approximations to the conditional probability functions, piy =j ). 

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. 

The similarity in approach to LDA (Section 33.2.2) and PLS (Section 33.2.8) should be pointed out. Neural classification networks are related to neural regression networks in the same way that PLS can be applied both for regression and classification and that LDA can be described as a regression application. This can be generalized all regression methods can be applied in pattern recognition. One must expect, for instance, that methods such as ACE and MARS (see Chapter 11) will be used for this purpose in chemometrics. 

Most of the supervised pattern recognition procedures permit the carrying out of stepwise selection, i.e. the selection first of the most important feature, then, of the second most important, etc. One way to do this is by prediction using e.g. cross-validation (see next section), i.e. we first select the variable that best classifies objects of known classification but that are not part of the training set, then the variable that most improves the classification already obtained with the first selected variable, etc. The results for the linear discriminant analysis of the EU/HYPER classification of Section 33.2.1 is that with all 5 or 4 variables a selectivity of 91.4% is obtained and for 3 or 2 variables 88.6% [2] as a measure of classification success. Selectivity is used here. It is applied in the sense of Chapter... 

D. Coomans and D.L. Massart, Alternative K-nearest neighbour rules in supervised pattern recognition. Part 2. Probabilistic classification on the basis of the kNN method modified for direct density estimation. Anal. Chim. Acta, 138 (1982) 153-165. 

MIR techniques have simplified obtaining infrared spectra of many materials important in packaging. These include rubber, plastics, laminations, and components of these materials that find use in pumps, sample packages, and devices. The combination of MIR and computerized pattern recognition techniques can be used for differentiating and classification of flexible packaging polymers such as polyvinyl chloride (PVC), polyvinylidene chloride (PVdC), acrylonitrile (Barex), and CTFE (Aclar) [22]. 

H. Liu. Classifications of PYC for pharmaceutical blister packaging using pattern recognition techniques. Ph.D. dissertation, Rutgers University, New Brunswick, NJ, 1998. 

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