An adaptive incremental approach to constructing ensemble classifiers: application in an information-theoretic computer-aided decision system for detection of masses in mammograms.
An adaptive incremental approach to constructing ensemble classifiers: application in an information-theoretic computer-aided decision system for detection of masses in mammograms.
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DOI：
10.1118/1.3132304
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发表时间：
2009-07
期刊：
影响因子：
3.8
通讯作者：
M. Mazurowski;J. Zurada;G. Tourassi
中科院分区：
文献类型：
--
作者：
M. Mazurowski;J. Zurada;G. Tourassi
集成分类器已在多种应用中被证明是高效的。在本文中，作者探讨了集成分类器在基于病例的计算机辅助诊断系统中检测乳房 X 光检查中肿块的有效性。他们通过对可用开发数据集进行重新采样来评估构建子分类器的两种通用方法：随机划分和随机选择。此外，他们讨论了选择集成大小的问题，并提出了两种自适应增量技术，可以自动选择当前问题的大小。所有技术均根据先前提出的信息论 CAD 系统 (IT-CAD) 进行评估。实验结果表明，与原始 IT-CAD 系统 (AUC = 0.865 +/- 0.029) 相比，所检查的集成技术在性能方面提供了统计上显着的改进 (AUC = 0.905 +/- 0.024)。一些技术可以显着减少案例库中存储的示例总数（减少到原始大小的 1.3%），从而降低存储要求并缩短系统的响应时间。在本文研究的方法中，提出的两种自适应技术是迄今为止对此目的最有效的。此外，作者还为选择集成参数提供了一些讨论和指导。
Ensemble classifiers have been shown efficient in multiple applications. In this article, the authors explore the effectiveness of ensemble classifiers in a case-based computer-aided diagnosis system for detection of masses in mammograms. They evaluate two general ways of constructing subclassifiers by resampling of the available development dataset: Random division and random selection. Furthermore, they discuss the problem of selecting the ensemble size and propose two adaptive incremental techniques that automatically select the size for the problem at hand. All the techniques are evaluated with respect to a previously proposed information-theoretic CAD system (IT-CAD). The experimental results show that the examined ensemble techniques provide a statistically significant improvement (AUC = 0.905 +/- 0.024) in performance as compared to the original IT-CAD system (AUC = 0.865 +/- 0.029). Some of the techniques allow for a notable reduction in the total number of examples stored in the case base (to 1.3% of the original size), which, in turn, results in lower storage requirements and a shorter response time of the system. Among the methods examined in this article, the two proposed adaptive techniques are by far the most effective for this purpose. Furthermore, the authors provide some discussion and guidance for choosing the ensemble parameters.