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Twins Decision Tree Classification: A Sophisticated Approach to Decision Tree Construction

机译：双胞胎决策树分类：决策树建设的复杂方法

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Decision tree classification is one of the most practical and effective methods which is used in inductive learning. Many different approaches, which are usually used for decision making and prediction, have been invented to construct decision tree classifiers. These approaches try to optimize parameters such as accuracy, speed of classification, size of constructed trees, learning speed, and the amount of used memory. There is a trade off between these parameters. That is to say that optimization of one may cause obstruction in the other, hence all existing approaches try to establish equilibrium. In this study, considering the effect of the whole data set on class assigning of any data, we propose a new approach to construct not perfectly accurate, but less complex trees in a short time, using small amount of memory. To achieve this purpose, a multi-step process has been used. We trace the training data set twice in any step, from the beginning to the end and vice versa, to extract the class pattern for attribute selection. Using the selected attribute, we make new branches in the tree. After making branches, the selected attribute and some records of training data set are deleted at the end of any step. This process continues alternatively in several steps for remaining data and attributes until the tree is completely constructed. In order to compare this new approach with previous ones we used some known data sets which have been used in different researches. This approach has been compared with others based on the classification accuracy and also the decision tree size. Experimental results show that it is efficient to use this approach particularly in cases of massive data sets, memory restrictions or short learning time.
机译：决策树分类是在归纳学习中使用的最实用和有效的方法之一。已经发明了许多通常用于决策和预测的方法以构建决策树分类器。这些方法尝试优化参数，例如准确性，分类速度，构造的树木大小，学习速度，学习速度以及使用的存储器量。这些参数之间存在权衡。也就是说，优化一个人可能导致另一个障碍物，因此所有现有方法都尝试建立均衡。在本研究中，考虑到整个数据在类分配上的所有数据的效果，我们提出了一种新方法，在短时间内构建不完全准确，但不太复杂的树木，使用少量内存。为实现此目的，已经使用了多步骤。我们在任何步骤中追踪训练数据两次，从开始到结束，反之亦然，以提取属性选择的类模式。使用所选属性，我们在树中制作新的分支。在制作分支机构后，在任何步骤结束时删除所选属性和一些训练数据集的记录。此过程在剩余数据和属性的几个步骤中继续，直到树完全构造。为了将这种新方法与先前的方法进行比较，我们使用了一些已知的数据集，这些数据集已用于不同的研究。基于分类准确性以及决策树大小，将这种方法与他人进行比较。实验结果表明，在大规模数据集，内存限制或短学习时间的情况下，使用这种方法是有效的。

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