Iticategory Classification

Three different strategies are possible if a series of binary decisions is used to solve a mu Iticategory problem C85T  

In this method each binary classifier is trained to classify patterns into one of two classes separated by a cutoff point. Class 1 means 

TABLE 4. Parallel arrangement of binary classifiers for the prediction of the number of atoms of a certain element in the molecule. 

The number of the class 02) is coded as a binary number. For each binary digit/ a binary classifier is trained that predicts zero or one . This classification method requires the smallest number of binary classifiers C85/ 1783. For example/ up to 8 classes can be discriminated by 3 binary classifiers (Table 5). The accuracy of the method can be improved by introducing additional binary digits (additional binary classifiers) to form an error-correcting code analogous to a parity bit . 

However/ for some classifiers (binary digits)/ a physically meaningful 

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In certain classification problems, a linear separation of two classes by only one decision plane is impossible. Figure 27 shows a two-modal class (+) consisting of two distinct clusters (subclasses). Evidently, this class should be represented by two prototypes (Wg/ w ) and a minimum distance classifier would be successful. In this way, the pattern space is partitioned by several decision planes (piecewise-linear separation). Classification of an unknown pattern requires the calculation of the scalar products with all weight vectors (prototype vectors). The unknown is assigned to the class with the largest scalar product (Chapter 2.1.5.). In the same way, a mu Iticategory classification is possible C89, 3963. 

The Bayes strategy C376, 389, 396, 4033 is a method for making optimum decisions. For simplicity, the method is described for a binary classification but it may also be used for mu Iticategory problems. 

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