• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Medical Physics >An adaptive incremental approach to constructing ensemble classifiers: application in an information-theoretic computer-aided decision system for detection of masses in mammograms.
【24h】

An adaptive incremental approach to constructing ensemble classifiers: application in an information-theoretic computer-aided decision system for detection of masses in mammograms.

机译：一种自适应的增量方法来构建整体分类器：在信息理论计算机辅助决策系统中用于检测乳房X线照片质量的应用。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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.
机译：集成分类器已在多种应用中显示有效。在本文中，作者探索了在基于案例的计算机辅助诊断系统中检测乳腺X线照片中肿块的整体分类器的有效性。他们通过对可用开发数据集进行重采样来评估构造子分类器的两种通用方法：随机划分和随机选择。此外，他们讨论了选择整体大小的问题，并提出了两种自适应增量技术，这些技术可以自动为当前问题选择大小。相对于先前提出的信息理论CAD系统（IT-CAD）评估了所有技术。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上具有统计学上的显着提高（AUC = 0.905 +/- 0.024）。某些技术可以显着减少存储在案例库中的示例总数（降至原始大小的1.3％），从而导致较低的存储需求和较短的系统响应时间。在本文研究的方法中，提出的两种自适应技术是迄今为止最有效的方法。此外，作者为选择整体参数提供了一些讨论和指导。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号