Training sets discriminant analysis

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... 

An examination of the results of the external predictions shows interesting findings. The compounds ofTable 17.1 that are projected in the Discriminant Analysis-PLS model made with the training set compounds are shown in Fig. 17.2 (the... 

More recently, another linear discriminant analysis (LDA) model was constructed for a set of 157 compounds for which Pcaco-2 was measured [43]. This model, which applied DRAGON descriptors, achieved an accuracy of classification at 91 % for the training set and 84% for the test set. When this model was applied to predict a set of 241 drugs for which HIA data were available, good correlation (>81%) was achieved between the two ADME-Tox properties. 

It may be possible to use an array of electrodes containing various enzymes in combination with multivariate statistical analyses (principal component analysis, discriminant analysis, partial least-squares regression) to determine which pesticide(s) the SPCE has been exposed to and possibly even how much, provided sufficient training sets of standards have been measured. The construction methods for such arrays would be the same as described in this protocol, with variations in the amounts of enzyme depending on the inhibition constants of other cholinesterases for the various pesticides of interest. 

Supervised methods rely on some prior training of the system with objects known to belong to the class they define. Such methods can be of the discriminant or modeling types.11 Discriminant methods split the pattern space into as many regions as the classes encompassed by the training set and establish bounds that are shared by the spaces. These methods always classify an unknown sample as a specific class. The most common discriminant methods include discriminant analysis (DA),12 the K-nearest neighbor... 

For analysis of more complicated tablet matrices, it is unlikely that training sets of 14 or fewer samples would suffice in the development of robust models for quantitation or qualitative discrimination. Considering a typical tablet with, say,... 

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