Decision tree for uncertainty measures
Decision tree for uncertainty measures
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不确定性度量的决策树
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Chebre
中科院分区：
文献类型：
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作者：
Myriam Tami;M. Clausel;Emilie Devijver;Éric Gaussier;J. Aubert;Chebre
集成方法是一种流行的机器学习技术，当人们想要处理分类或预测问题时，它是强大的。构造一组分类器（回归或分类树），并通过跟踪加权投票来完成新数据实例的分类或预测。树是从数据中获得的分区上的分段常数估计。这些分区是由输入变量集的递归并矢分裂引起的。例如，CART（分类和回归树）[1]是一种构造树的有效算法。目标是在尽可能多的“同质”K不相交区域中划分输入变量值的空间。更准确地说，每个划分值必须最小化风险函数。然而，在实践中，实验测量可以观察到不确定性。本文提出将CART算法推广到这类数据。我们提出了一个适用于不确定性数据的诱导模型，并考虑到数据库中每个定量观测的不确定性，为树的构建提供了预测和分裂规则。
The ensemble methods are popular machine learning techniques which are powerful when one wants to deal with both classification or prediction problems. A set of classifiers (regression or classification trees) is constructed, and the classification or the prediction of a new data instance is done by tacking a weighted vote. A tree is a piece-wise constant estimator on partitions obtained from the data. These partitions are induced by recursive dyadic split of the set of input variables. For example, CART (Classification And Regression Trees) [1] is an efficient algorithm for the construction of a tree. The goal is to partition the space of input variable values in the most as possible "homogeneous" K disjoint regions. More precisely, each partitioning value has to minimize a risk function. However, in practice, experimental measures can be observed with uncertainty. This work proposes to extend CART algorithm to this kind of data. We present an induced model adapted to uncertainty data and both a prediction and split rule for a tree construction taking into account the uncertainty of each quantitative observation from the data base.