Bagging tree classifiers for laser scanning images: a data- and simulation-based strategy
Bagging tree classifiers for laser scanning images: a data- and simulation-based strategy
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DOI：
10.1016/s0933-3657(02)00085-4
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发表时间：
2003-01-01
影响因子：
7.5
通讯作者：
Lausen, B
中科院分区：
文献类型：
--
作者：
Hothorn, T;Lausen, B
基于医学图像数据的诊断在医学决策制定和临床常规中是常见的。我们讨论了一种策略，以获得一个具有良好的临床图像数据的性能分类器，并证明分类器的性能的图像数据的自适应模拟模型。我们专注于问题的分类眼睛为正常或神经性昏迷的基础上，来自激光扫描图像的视神经乳头的62个常规解释变量。作为学习样本，我们使用了一个病例对照研究的98名正常人和98名昏迷受试者的年龄和性别相匹配。聚合多个不稳定的分类器允许大量减少误分类错误在许多应用程序和基准问题。我们研究了临床学习样本以及眼睛形态模拟模型的各种分类器的性能。Bagged分类树（Bagged-CTREE）与单分类树和线性判别分析（LDA）进行了比较。我们还比较了误分类误差的三种估计：10倍交叉验证，0.632+自助法和袋外估计。总之，我们的基于知识的决策支持策略的应用表明，袋装分类树对青光眼分类表现最好。(C)2002 Elsevier Science B. V.保留所有权利。
Diagnosis based on medical image data is common in medical decision making and clinical routine. We discuss a strategy to derive a classifier with good performance on clinical image data and to justify the properties of the classifier by an adapted simulation model of image data. We focus on the problem of classifying eyes as normal or glaucomatous based on 62 routine explanatory variables derived from laser scanning images of the optic nerve head. As learning sample we use a case-control study of 98 normal and 98 glaucomatous subjects matched by age and sex.Aggregating multiple unstable classifiers allows substantial reduction of misclassification error in many applications and bench mark problems. We investigate the performance of various classifiers for the clinical learning sample as well as for a simulation model of eye morphologies. Bagged classification trees (bagged-CTREE) are compared to single classification trees and linear discriminant analysis (LDA). We additionally compare three estimators of misclassification error: 10-fold cross-validation, the 0.632+ bootstrap and the out-of-bag estimate. In summary, the application of our strategy of a knowledge-based decision support shows that bagged classification trees perform best for glaucoma classification. (C) 2002 Elsevier Science B.V. All rights reserved.