关于确定性和非确定性决策树的复杂性,用于来自封闭类的常规决策表
Azimkhon Ostonov1, Mikhail Moshkov1
1Computer, Electrical and Mathematical Sciences & Engineering Division and Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
本研究研究了从决策表中得出的决策树. 我们分析了属性复杂性如何影响确定性和非确定性决策树的最小复杂性.
科学领域:
- 计算机科学 计算机科学
- 决策理论 决策理论
- 算法复杂性 算法复杂性
背景情况:
- 决策表对于表现复杂的决策过程至关重要.
- 了解决策树的复杂性对于高效的算法至关重要.
- 以前的研究已经探索了决策树优化,但决策表的封闭类的影响需要进一步调查.
研究的目的:
- 分析属性复杂度与确定性和非确定性决策树的最小复杂度之间的关系.
- 研究确定性和非确定性决策树的最小复杂度之间的相关性.
- 在封闭类中扩大对决策表属性的理解.
主要方法:
- 考虑属于属性删除和决策修改下关闭类的常规决策表.
- 对决策树复杂性与属性集复杂性的依赖性的分析.
- 确定性与非确定性决策树的最小复杂性的比较研究.
主要成果:
- 建立了属性集的复杂性和确定性和非确定性决策树的最小复杂性之间的依赖关系.
- 量化了确定性决策树及其非确定性对应的最小复杂度之间的关系.
- 证明非确定性决策树可以代表涵盖所有表列的真决策规则集.
结论:
- 决策树的复杂性受到决策表的封闭类内底层属性集的复杂性的重大影响.
- 该研究提供了关于确定性和非确定性决策树表示之间的权衡的见解.
- 这些发现有助于对决策表分析和决策树构建的更深入的理论理解.
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