区间值属性的单调决策树算法

目前存在的一些区间值属性决策树算法都是在无序情况下设计的，未考虑条件属性和决策属性之间的序关系。针对这些算法处理有序分类问题的不足，提出区间值属性的单调决策树算法，用于处理区间值属性的单调分类问题。该算法利用可能度确定区间值属性的序关系，使用排序互信息度量区间值属性的单调一致程度，通过排序互信息的最大化选取扩展属性。此外，将非平衡割点应用到区间值属性决策树构建过程中，减少排序互信息的计算次数，提高计算效率。实验表明文中算法提高了效率和测试精度。

Some learning algorithms of interval-valued attributes are developed in the disorderly situation. The ordinal relation between condition attributes and decision attributes is not taken into account. In this paper, aiming at the defects of the original algorithms, a monotonic decision tree algorithm is proposed to deal with monotonic classification of interval-valued attributes. The possibility degree is used to determine the order relation of interval-valued attributes, the rank mutual information is utilized to measure the monotonic consistency, and the expanded attributes are selected by maximizing the rank mutual information. Furthermore, unstable cut-points are applied to the construction process of interval-valued attributes decision tree to reduce the computing number of rank mutual information and improve the computational efficiency. The experimental results show that the algorithm improves the efficiency and testing accuracy.