Commonly encountered classification methods

Some common classification parameters are the mean values of the classes in the classification space, the variance of the class s calibration samples around the class mean, and the unmodeled variance in the calibration samples. Classification logic varies widely between classification methods. The following section provides details on some commonly encountered classification methods. 

The KNN method is probably the simplest classification method to understand. It is most commonly applied to a principal component space. In this case, calibration is achieved by simply constructing a PCA model using the calibration data, and choosing the optimal number of PCs (A) to use in the model. Prediction of an unknown sample is then done by calculating the PC scores for that sample (Equation 8.57), followed by application of the classification rule. 

The KNN classification rule involves rather simple logic  

The KNN method has several advantages aside from its relative simplicity. It can be used in cases where few calibration data are available, and can even be used if only a single calibration sample is available for some classes. In addition, it does not assume that the classes are separated by linear partitions in the space. As a result, it can be rather effective at handling highly non-linear separation structures. 

One disadvantage of the KNN method is that it does not provide an assessment of confidence in the class assignment result. In addition, it does not sufficiently handle the 

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To illustrate some commonly encountered classification methods, a data set obtained from a series of polyurethane rigid foams will be used [76]. In this example, a series of 26 polyurethane foam samples were analyzed by NIR diffuse reflectance spectroscopy. The spectra of these foams are shown in Figure 12.16. Each of these foam samples belongs to one of four known classes, where each class is distinguished by... 

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