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Home Multivariate Data Modeling Classification and Discrimination Discrimination and Classification  
See also: linear discriminant analysis, neural networks, KNN classification  
Discrimination and Classification
Discrimination techniques are methods which use measured variables to put the observations into different classes. The important difference between multiple regression models and discrimination models is that the dependent variable is some kind of class number. In many cases the objects may be members of only two possible classes, which simplifies the discrimination problem to a binary classification. An example would be a good/bad test in quality inspection.
The classification problem can be interpreted in a geometrical way:
find a proper line (or curve) of separation between two or more groups
of similar objects. The type of separating line depends on the method used.
A neural network, for instance, could create a nonlinear discriminating
function, whereas the LDA creates only linear surfaces. This of course
implies that not every method is equally wellsuited to a particular problem.
The classification problem as shown below may be easily solved by a neural
network while, for example, LDA would fail.


Home Multivariate Data Modeling Classification and Discrimination Discrimination and Classification 
Last Update: 20121008