Inductive Classi cation Using Taxonomy

We discuss how to classify input inductively where we assume taxonomy on attributes. Although we learn classi cation rules inductively by means of decision tree generation, we can't utilize the generation and testing methodologies for this purpose since each input data carries multiple roles based on taxonomy. In this investigation we introduce Decision Tree with Hierarchy (DTH) to class and attributes; to each attribute we assume taxonomy on the domain in addition to class hierarchy. Then, we extend information-theoretic de nition that causes nonexclusive decision trees to test inputs, and we discuss a new algorithm to examine which paths should be examined. Content Areas { Machine Learning, AI in Databases, AI in Data Mining