Performance Measurement of Decision Tree Excluding Insignificant Leaf Nodes
Performance Measurement of Decision Tree Excluding Insignificant Leaf Nodes
复制标题
排除不重要叶节点的决策树的性能测量
DOI：
10.1109/cyberc.2014.29
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
W. Lee
中科院分区：
文献类型：
--
作者：
Hae Sook Jeon;W. Lee
无处不在的环境中存在太多的信息，因此从可用的数据集中获得适当分类的信息是不容易的。决策树算法在数据挖掘或机器学习系统中有着广泛的应用，它在分类问题上具有速度快、结果好等优点。然而，有时决策树可能具有仅由少数数据或噪声数据组成的叶节点。这些弱叶所做的决策将是无效的，因此应该被排除在决策过程之外。本文提出了一种使用分类器UChoo解决分类问题的方法，并提出了一种只涉及重要叶子从而排除噪声叶子的有效决策过程方法。实验表明，该方法是有效的，减少了错误的决策，并可以应用于只有重要的决策。
Too much information exist in ubiquitous environment, and therefore it is not easy to obtain the appropriately classified information from the available data set. Decision tree algorithm is useful in the field of data mining or machine learning system, as it is fast and deduces good result on the problem of classification. Sometimes, however, a decision tree may have leaf nodes which consist of only a few or noise data. The decisions made by those weak leaves will not be effective and therefore should be excluded in the decision process. This paper proposes a method using a classifier, UChoo, for solving a classification problem, and suggests an effective method of decision process involving only the important leaves and thereby excluding the noisy leaves. The experiment shows that this method is effective and reduces the erroneous decisions and can be applied when only important decisions should be made.