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Decision Degree-based Decision Tree Technology for Rule Extraction

机译：基于决策的决策树技术

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—Traditional rough set-based approaches to reduct have difficulties in constructing optimal decision tree, such as empty branches and over-fitting, selected attribute with more values, and increased expense of computational effort. It is necessary to investigate fast and effective search algorithms. In this paper, to address this issue, the limitations of current knowledge reduction for evaluating decision ability are analyzed deeply. A new uncertainty measure, called decision degree, is introduced. Then, the attribute selection standard of classical heuristic algorithm is modified, and the new improved significance measure of attribute is proposed. A heuristic algorithm for rule extraction from decision tree is designed. The advantages of this method for rule extraction are that it needn’t compute relative attribute reduction of decision tables, the computation is direct and efficient, and the time complexity is much lower than that of some existing algorithms. Finally, the experiment and comparison show that the algorithm provides more precise and simplified decision rules. So, the work of this paper will be very helpful for enlarging the application areas of rough set theory.
机译：基于粗糙的集合来减少的方法在构建最佳决策树（例如空分支和过度拟合）的困难方面具有困难，例如具有更多值的空分支和过度拟合，选择的属性，并增加计算工作的费用。有必要调查快速有效的搜索算法。在本文中，为了解决这个问题，深入分析了评估决策能力的当前知识减少的局限性。介绍了一种新的不确定性措施，称为决策程度。然后，修改了经典启发式算法的属性选择标准，提出了新的改进的属性意义测量。设计了一种决策树的规则提取的启发式算法。这种规则提取方法的优点是它不需要计算决策表的相对属性减少，计算是直接且有效的，并且时间复杂程度远低于某些现有算法的时间。最后，实验和比较表明该算法提供了更精确和简化的决策规则。因此，本文的工作将非常有助于扩大粗糙集理论的应用领域。

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