論理最小化に基づく決定木による知識発見(発見科学)

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We propose General-purposive decision tree generation algorithm(MINI based TREE, named MINI-TREE), which guarantees to obtain quasi-optimum solution with less computational complexity even in worst condition to develop decision tree. In inductive learning, decision tree is widely known as one of the representative knowledge representation scheme. Because of effectiveness, decision tree is used in various application fields. ID3 and C4.5 are famous and typical top-down algorithms, which create the decision tree from the root node by evaluating the amount of gain information of each attribute and deciding which attribute is fitted in the active node. However, they can maintain its performance, only when each attribute is assumed to be semantically independent in attributes-selection scheme from gain information. Therefore, the performance of them remarkably lowers if there exists strong correlation between some attributes in a set of examples. MINITREE is a top-down algorithm that decides to select the attributes by the selection-criterion using the logical formula minimization process of logic minimization algorithm named MINI. By applying logic minimization algorithm, the selection of the attributes as an importance becomes possible in order to develop the compact decision tree in each attributes selection step, even if it is the attribute inferior in respect of gain information. Especially, MINITREE is shown to be much useful to mass data including strongly correlative attributes.
社団法人人工知能学会の論文
2000-07-01

論理最小化に基づく決定木による知識発見(発見科学)

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