NEIGHBORHOOD GRAPHS IN CLASSIFICATION PROBLEMS FOR SYMBOLIC DATA(Symbolic Data Analysis)

This paper presents new neighborhood graphs useful to solve feature selection problems in pattern recognition for symbolic data. In pattern recognition for symbolic data, each sample pattern is described not only by quantitative features but also by qualitative features. We introduce the Cartesian System Model (CSM) as a mathematical model to treat symbolic data. Then, we define the Generality Ordered Mutual Neighborhood Graph and the Generality Ordered Interclass Mutual Neighborhood Graph based on the CSM. These neighborhood graphs play central roles in seeing details of the interclass structures. We outline the basic idea of our classifier by using simple examples.