Input data for decision trees
Input data for decision trees
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决策树的输入数据
DOI：
10.1016/j.eswa.2006.12.030
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发表时间：
2008
影响因子：
8.5
通讯作者：
S. Piramuthu
中科院分区：
文献类型：
--
作者：
S. Piramuthu
数据挖掘已经成功地应用于各种各样的应用领域。数据挖掘本身使用几种不同的方法完成。决策树是数据挖掘中常用的方法之一。由于构建这些决策树的过程假设数据中没有分布模式（非参数），因此输入数据的特征通常不会受到太多关注。我们考虑了输入数据的一些特性及其对决策树学习性能的影响。初步结果表明，决策树的性能可以提高与输入数据的微小修改。
Data Mining has been successful in a wide variety of application areas for varied purposes. Data Mining itself is done using several different methods. Decision Trees are one of the popular methods that have been used for Data Mining purposes. Since the process of constructing these decision trees assume no distributional patterns in the data (non-parametric), characteristics of the input data are usually not given much attention. We consider some characteristics of input data and their effect on the learning performance of decision trees. Preliminary results indicate that the performance of decision trees can be improved with minor modifications of input data.