粗糙集和决策树都属于归纳学习方法,都可以从一个离散值决策表中抽取出规则.本文从算法过程、计算复杂性、规则个数、泛化能力、稳健性几个方面对粗糙集和决策树进行了比较研究,得出了一些重要结论,能为相关研究提供一些有价值的参考.
Rough sets and decision trees are both inductive learning methods, and can extract rules from a decision table with discrete values. In this paper, we compare rough sets with decision trees in the following aspects: process of algorithm, computational complexity, number of rules, generalization abilities and robustness. Some important conclusions have been obtained, which can provide valuable reference for further research works