1995 年 10 巻 1 号 p. 141-146
The ID3 algorithms provide robust inductive processes of learning concepts from examples by constructing decision trees. The standard ID3 algorithm, however, is restricted to utilize symbolic/numeric attributes, and receive non-structured examples. We here extend the algorithm so that it can treat hierarchical attributes, and can receive structured examples (A hierarchical attribute relates its values hierarchically, and a structured example is an example having more than one component). The first Problem is solved by finding adaptively appropriate values for getting a target decision tree based on the formulated value generalization process. The second is by introducing a new type of attribute-based descriptions in which any attribute refers to some specified components Computational experiments are also examined to show the validity of the proposed methods.
