不完备区间值决策系统的三支决策模型及增量式规则获取算法

在现实应用中，区间值数据会因为测量、干扰或信息传输等噪声影响导致数据出现缺失值，而且这些数据随着时间推移呈现动态递增趋势，忽略或删除这些数据很有可能导致有用信息的丢失而出现决策误判。为此，针对这一问题，提出面向不完备区间值决策系统的三支决策模型和增量式规则获取算法。首先定义不完备区间值数据的量化相似容差关系，构造出基于不完备区间值决策系统的三支决策模型；其次从两个层级分析对象集动态规则获取策略，提出增量式规则获取算法；最后，通过一组UCI数据集对该算法进行验证。实验结果表明，该算法不仅能减少误划分损失获得更高的划分精度，而且在运行时间上也具有较大优越性。

Three-way decisions model and incremental rule acquisition algorithm for incomplete interval-valued decision system

In practical applications, interval data will have missing values due to measurement, interference, information transmission and other noise effects, and these data show a dynamic increasing trend over time, ignore or delete these data likely lead to the loss of useful information and misjudgment of decision making. To solve this problem, this paper proposed a three-way decisions model and an incremental rule acquisition algorithm for an incomplete interval-valued decision system. Firstly, it defined the quantitative similarity tolerance relationship of incomplete interval value data, and constructed a three-way decisions model based on the incomplete interval-valued decision system. Secondly, this paper analyzed two levels of dynamic rules of the object set to obtain the policy, and proposed an incremental rule acquisition algorithm. Finally, it used a set of UCI data sets to verify the algorithm. Experimental results show that this algorithm can not only reduce the loss of partition error, but also achieve higher partition accuracy, and has great advantages in running time.