Selected Classification Methods

Abstract

Any classification algorithm should consists of some classifiers together with a method of conflicts resolving between the classifiers when new objects are classified. In this chapter we discuss two classes of classification algorithms. Algorithms from the first class are using sets of decision rules as classifiers together with some methods of conflict resolving. The rules are generated from decision tables with tolerance relations using Boolean reasoning approach. They create decision classes descriptions. However, to predict (or classify) new object to a proper decision class it is necessary to fix some methods for conflict resolving between rules recognizing the object and voting for different decisions. We also discuss how such decision rules can be generated using Boolean reasoning. Algorithms of the second kind are based on the nearest neighbor method (k − NN) (see, top ten data mining algorithms [217]). We show how this method can be combined with some rough set method for relevant attribute selection.