Feature selection

The general problem of constructing logical recognition and classification trees is considered. The object of this study is logical classification trees. The subject of the research is current methods and algorithms for constructing logical classification trees. The aim of the work is to create a simple and effective method for constructing recognition models based on classification trees for training samples of discrete information, which is characterized by elementary features in the structure of synthesized logical classification trees.

In the paper, a classification method is proposed. It is based on Combined Swarm Negative Selection Algorithm, which was originally designed for binary classification problems. The accuracy of developed algorithm was tested in an experimental way with the use of microarray data sets. The experiments confirmed that direction of changes introduced in developed algorithm improves its accuracy in comparison to other classification algorithms.