Classifier discriminant

In discriminant analysis, in a manner similar to factor analysis, new synthetic features have to be created as linear combinations of the original features which should best indicate the differences between the classes, in contrast with the variances within the classes. These new features are called discriminant functions. Discriminant analysis is based on the same matrices B and W as above. The above tested groups or classes of data are modeled with the aim of reclassifying the given objects with a low error risk and of classifying ( discriminating ) another objects using the model functions. 

Artificial Intelligence-based classifiers discriminating drugs from non-drugs... 

Woodruff and co-workers introduced the expert system PAIRS [67], a program that is able to analyze IR spectra in the same manner as a spectroscopist would. Chalmers and co-workers [68] used an approach for automated interpretation of Fourier Transform Raman spectra of complex polymers. Andreev and Argirov developed the expert system EXPIRS [69] for the interpretation of IR spectra. EXPIRS provides a hierarchical organization of the characteristic groups that are recognized by peak detection in discrete ames. Penchev et al. [70] recently introduced a computer system that performs searches in spectral libraries and systematic analysis of mixture spectra. It is able to classify IR spectra with the aid of linear discriminant analysis, artificial neural networks, and the method of fe-nearest neighbors. 

Discriminant emalysis is a supervised learning technique which uses classified dependent data. Here, the dependent data (y values) are not on a continuous scale but are divided into distinct classes. There are often just two classes (e.g. active/inactive soluble/not soluble yes/no), but more than two is also possible (e.g. high/medium/low 1/2/3/4). The simplest situation involves two variables and two classes, and the aim is to find a straight line that best separates the data into its classes (Figure 12.37). With more than two variables, the line becomes a hyperplane in the multidimensional variable space. Discriminant analysis is characterised by a discriminant function, which in the particular case of hnear discriminant analysis (the most popular variant) is written as a linear combination of the independent variables ... 

The surface that actually separates the classes is orthogonal to this discriminant function, as shown in Figure 12.37, and is chosen to maximise the number of compounds correctly classified. To use the results of a discriminant analysis, one simply calculates the appropriate value of the discriminant function, from which the class can be determined. 

One of the problems with discrimination-oriented methods is that we need to classify each object in one of the given classes. It is, however, quite possible that an... 

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... 

Because a hyperplane corresponds to a boundary between pattern classes, such a discriminant function naturally forms a decision rule. The global nature of this approach is apparent in Fig. 19. An infinitely long decision line is drawn based on the given data. Regardless of how closely or distantly related an arbitrary pattern is to the data used to generate the discriminant, the pattern will be classified as either o>i or <02. When the arbitrary pattern is far removed from the data used to generate the discriminant, the approach is extremely prone to extrapolation errors. 

The curve illustrates the sharpness of tests depending on the discrimination limit. In this way, TPR and TNR may be recognized and the unreliability region around the limit of specification can be estimated. Beyond the limits of the unreliability interval, it is possible to classify samples correctly apart... 

A model was built for discriminating the five distinct obsidian localities (Coso, Mt. Hicks, Fish Springs, Shoshone, and Bodie Hills), the broadband LIBS spectra for which exhibit a high degree of visual similarity 100% of the 200 test single-shot spectra were correctly classified with that model (Table 1). In other words, obsidian samples from the 5 regions are easily distinguishable with LIBS. 

Fig. 8.4 Discriminant analyses of the principal chemical components in L. catta scent secretions by (a) gland, (b) season, and (c) individual, (a) Accurate classification of 97.5% of labial, scrotal, and brachial samples in = 77) by gland of origin (Wilks lambda = 0.003 P < 0.001). (b) Reliable differentiation of 100% of labial samples (n = 26) into prebreeding, breeding, and nonbreeding seasons (Wilks lambda = 0.018, P < 0.01). (c) Individual scent signatures in the scrotal secretions from seven males. LDA performed on 17 principal components correctly classified 100% of these samples to the individuals from which they were collected (Wilks lambda = 0.000, P < 0.002)...

By means of intracellular recording and staining methods, we have examined the responses of AL neurons to stimulation of the ipsilateral antenna with each of the sex pheromone components as well as partial and complete blends (75). In accordance with results of behavioral and sensory-receptor studies, components A and B are the most effective and potent sex pheromone components for eliciting physiological responses in the male-specific AL neurons. On the basis of these responses, we classified the neurons into two broad categories pheromone generalists and pheromone specialists (76). Pheromone generalists are neurons that respond similarly to stimulation of either the component A input channel or the component B input channel and do not respond differently when the complete, natural pheromone blend is presented to the antenna. In contrast, pheromone specialists are neurons that can discriminate between antennal stimulation with component A and stimulation with component B. There are several types of pheromone specialists. Some... 

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