Focal-Test-Based Spatial Decision Tree Learning
Focal-Test-Based Spatial Decision Tree Learning
复制标题
基于焦点测试的空间决策树学习
DOI：
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发表时间：
2015
影响因子：
8.9
通讯作者：
J. Corcoran
中科院分区：
文献类型：
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作者：
Zhe Jiang;S. Shekhar;Xun Zhou;Joseph K. Knight;J. Corcoran
给定来自栅格数据集的学习样本，空间决策树学习的目标是找到一个最小化分类错误和椒盐噪声的决策树分类器。这一问题具有重要的社会应用，例如用于自然资源管理的土地覆被分类。然而，这个问题是具有挑战性的，因为学习样本在类标签中显示空间自相关，而不是独立同分布。相关工作依赖于本地测试（即，测试位置的特征信息），并且不能充分地对空间自相关效应进行建模，从而导致椒盐噪声。相比之下，我们最近提出了一个焦点测试为基础的空间决策树（FTSDT），其中的树遍历方向的样本是基于本地和焦点（邻域）的信息。初步结果表明，FTSDT减少了分类错误和椒盐噪声。本文扩展了我们最近的工作，引入了一个新的焦点测试方法，自适应的邻域，避免过度平滑的楔形区域。我们还通过在候选阈值之间重用焦点值来对FTSDT训练算法进行计算细化。理论分析表明，改进后的训练算法是正确的，具有更好的可扩展性。在真实的数据集上的实验结果表明，新的自适应邻域FTSDT提高了分类精度，我们的计算细化显着减少了训练时间。
Given learning samples from a raster data set, spatial decision tree learning aims to find a decision tree classifier that minimizes classification errors as well as salt-and-pepper noise. The problem has important societal applications such as land cover classification for natural resource management. However, the problem is challenging due to the fact that learning samples show spatial autocorrelation in class labels, instead of being independently identically distributed. Related work relies on local tests (i.e., testing feature information of a location) and cannot adequately model the spatial autocorrelation effect, resulting in salt-and-pepper noise. In contrast, we recently proposed a focal-test-based spatial decision tree (FTSDT), in which the tree traversal direction of a sample is based on both local and focal (neighborhood) information. Preliminary results showed that FTSDT reduces classification errors and salt-and-pepper noise. This paper extends our recent work by introducing a new focal test approach with adaptive neighborhoods that avoids over-smoothing in wedge-shaped areas. We also conduct computational refinement on the FTSDT training algorithm by reusing focal values across candidate thresholds. Theoretical analysis shows that the refined training algorithm is correct and more scalable. Experiment results on real world data sets show that new FTSDT with adaptive neighborhoods improves classification accuracy, and that our computational refinement significantly reduces training time.