Learning concept approximation from uncertain decision tables

Abstract

We present a hierarchical learning approach to approximation of complex concept from experimental data using inference diagram as a domain knowledge. The solution, based on rough set and rough mereology theory, require to design a learning method from uncertain decision tables. We examine the effectiveness of the proposed approach by comparing it with standard learning approaches with respect to different criteria on artificial data sets generated by a traffic road simulator.© Springer-Verlag Berlin Heidelberg 2005.