Algorithm for generating decision tree Based on adjoint positive region

Abstract

In this paper, all of the three relationships of attribute selection standard based on positive region, based on rough bound and based on attribute dependency are firstly analyzed. At the same time, it is proved that the three kinds of selection attribute standards are equivalent to each other. Furthermore the advantages and disadvantages of algorithm for generating decision tree based on positive region are analyzed. Meanwhile, aiming at these disadvantages, a new selection attribute standard based on adjoint positive region is proposed. The decision tree generated with the new standard of attribute selection has the following characteristics: fewer leaf nodes, fewer levels of average depth, better generalization of leaf nodes. Finally an example is used to illustrate the advantages of this new selection attribute standard. © Springer-Verlag Berlin Heidelberg 2013.