Classification methods individual algorithms

Theory. SIMCA is a parametric classification method introduced by Wold (29), which supposes that the objects of a given class are normally distributed. The particularity of this PCA-based method is that one model is built for each class separately, that is, disjoint class modeling is performed. The algorithm starts by determining the optimal number of PCs for each individual model with CV. The resulting PCs are then used to define a hypervolume for each class. The boundary around one group of objects is then the confidence limit for the residuals of all objects determined by a statistical T-test (30, 31). The direction of the PCs and the limits established for these PCs define the model of a class (Fig. 13.13). 

Discriminant Analysis (DA) is a multivariate statistical method that generates a set of classification functions that can be used to predict into which of two or more categories an observation is most likely to fall, based on a certain combination of input variables. DA may be more effective than regression for relating groundwater age to major ion hydrochemistry and well construction because it can account for complex, non-continuous relationships between age and each individual variable used in the algorithm while inherently coping with uncertainty in the age values used for... 

TTie classification of kinetic methods proposed by Pardue [18] is adopted in the software philosophy. TTie defined objective of measurement in the system is to obtain the best regression fit to a minimum of 10 data points, taken over either a fixed time (i.e. the maximum time for slow reactions) or variable time (for reactions complete in less than 34 min, which is the maximum practical observation time). In an analytical system generating information at the rate of SO datum points per second, with reactions being monitored for up to 2040 s, effective data-reduction is of prime importance. To reduce this large quantity of analytical data to more manageable proportions, an algorithm was devised to optimize the time-base of the measurements for each individual specimen. 

Fig. 1. Statistical classification strategy (SCS) a schematic road map of how the SCS method is developed for individual databases. GA ORS, genetic algorithm based optimal region selection LDA, linear discriminant analysis LOO, leave-one-out (method of cross-validation) coeff, coefficients.

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