The main contribution of this paper is the development of a new decision tree algorithm. The proposed approach allows users to guide the algorithm through the data partitioning process. We believe this feature has many applications but in this paper we demonstrate how to utilize this algorithm to analyse data sets containing missing values. We tested our algorithm against simulated data sets with various missing data structures and a real data set. The results demonstrate that this new classification procedure efficiently handles missing values and produces results that are slightly more accurate and more interpretable than most common procedures without any imputations or pre-processing.

中文翻译：

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最佳：处理缺失值的决策树算法

本文的主要贡献是开发了一种新的决策树算法。所提出的方法允许用户在数据分区过程中指导算法。我们相信该功能有许多应用，但是在本文中，我们演示了如何利用此算法来分析包含缺失值的数据集。我们针对具有各种缺失数据结构和真实数据集的模拟数据集测试了我们的算法。结果表明，这种新的分类程序可以有效处理缺失值，并且比没有任何估算或预处理的大多数常见程序生成的结果稍微更准确，更易于解释。