Classifying densities using functional regression trees: Applications in oceanology
Classifying densities using functional regression trees: Applications in oceanology
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DOI：
10.1016/j.csda.2006.09.028
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发表时间：
2007-06
期刊：
影响因子：
--
通讯作者：
D. Nerini;B. Ghattas
中科院分区：
文献类型：
--
作者：
D. Nerini;B. Ghattas
当响应变量是概率密度函数时，考虑构建回归树的问题。使用 Csiszár 的 f 散度提出了非常适合测量密度之间差异的分裂标准。通过数值模拟来比较采用不同标准构建的树木的性能。然后，构建一棵树，使用一组解释性环境变量来预测浮游动物群落的大小分布。使用函数 PCA 来解释每个终端节点中预测密度周围尺寸谱的主要变化模式。最后，使用装袋过程来提高基于树的模型的准确性。
The problem of building a regression tree is considered when the response variable is a probability density function. Splitting criteria which are well adapted to measure the dissimilarity between densities are proposed using the Csiszár's f-divergence. The comparison between performances of trees constructed with various criteria is tackled through numerical simulations. Afterwards, a tree is constructed to predict the size distribution of a zooplankton community using a set of explanatory environmental variables. Functional PCA is used in order to interpret the main modes of variation of the size spectra around the predicted density in each terminal node. Finally, a bagging procedure is used to increase the accuracy of the tree-based model.