Ensemble-based hybrid probabilistic sampling for imbalanced data learning in lung nodule CAD
Ensemble-based hybrid probabilistic sampling for imbalanced data learning in lung nodule CAD
复制标题
基于集成的混合概率采样，用于肺结节 CAD 中不平衡数据学习
DOI：
10.1016/j.compmedimag.2013.12.003
复制
发表时间：
2014-04-01
影响因子：
5.7
通讯作者：
Zaiane, Osmar
中科院分区：
文献类型：
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作者：
Cao, Peng;Yang, Jinzhu;Zaiane, Osmar
在肺结节计算机辅助检测（CAD）中，分类在降低假阳性（FPR）方面起着关键作用。FPR的困难在于结节外观的差异，以及结节类和非结节类之间分布的不均衡。此外，数据分布中存在固有复杂结构，例如类内不均衡和高维度，是降低分类性能的其他关键因素。为了解决这些挑战，我们提出了一种混合概率采样与多样随机子空间集成相结合的方法。实验结果表明，与常用方法相比，所提出的方法在几何均值（G - mean）和受试者工作特征曲线下面积（AUC）方面是有效的。（C）2013爱思唯尔有限公司。保留所有权利。
Classification plays a critical role in false positive reduction (FPR) in lung nodule computer aided detection (CAD). The difficulty of FPR lies in the variation of the appearances of the nodules, and the imbalance distribution between the nodule and non-nodule class. Moreover, the presence of inherent complex structures in data distribution, such as within-class imbalance and high-dimensionality are other critical factors of decreasing classification performance. To solve these challenges, we proposed a hybrid probabilistic sampling combined with diverse random subspace ensemble. Experimental results demonstrate the effectiveness of the proposed method in terms of geometric mean (G-mean) and area under the ROC curve (AUC) compared with commonly used methods. (C) 2013 Elsevier Ltd. All rights reserved.