決定木による階層属性を用いた概念の帰納学習

概要

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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.
社団法人人工知能学会の論文
1995-01-01