Tables, graphs and logic for induction

Abstract

Abstract Inductive learning, which tries to find rules from data, has been an important area of investigation. One major research theme of this area is the data representation language that the learning methods can use. Conventional learning methods use attributevalue tables as the data representation language, whereas inductive logic programming (ILP) uses first-order logic. We propose colored directed graphs as a data representation language for inductive learning methods. Graph-based induction (GBI) uses this data representation language. The expressiveness of graphs is in between the attributevalue table and the first-order logic. Thus its learning potential is weaker than that of ILP, but stronger than that of conventional attribute-value learning methods. The real advantage of GBI appears in the domain where the dependency between data bears the essential information.