The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support.
The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support.
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DOI：
10.1016/j.neunet.2011.07.002
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发表时间：
2012-01
期刊：
影响因子：
--
通讯作者：
Tourassi GD
中科院分区：
文献类型：
--
作者：
Malof JM;Mazurowski MA;Tourassi GD
案例选择是提高基于案例的分类器的效率和性能的有用方法。已经设计了多种技术来执行案例选择。本文实证研究了可用训练案例集中的类别不平衡如何影响最终分类器的性能以及所选集合的属性。在这项研究中，实验是使用一个数据集来进行的，该数据集用于解决在筛查乳房X光照片中检测乳房肿块的问题。分类问题是二元的，我们使用 k 最近邻分类器。使用接受者操作特征 (ROC) 曲线下面积 (AUC) 测量来评估分类器的性能。实验结果表明，尽管类别不平衡会降低派生分类器的性能以及选择在提高整体分类器性能方面的有效性，但无论类别不平衡的程度如何，案例选择仍然是有益的。
Case selection is a useful approach for increasing the efficiency and performance of case-based classifiers. Multiple techniques have been designed to perform case selection. This paper empirically investigates how class imbalance in the available set of training cases can impact the performance of the resulting classifier as well as properties of the selected set. In this study, the experiments are performed using a dataset for the problem of detecting breast masses in screening mammograms. The classification problem was binary and we used a k-nearest neighbor classifier. The classifier’s performance was evaluated using the Receiver Operating Characteristic (ROC) area under the curve (AUC) measure. The experimental results indicate that although class imbalance reduces the performance of the derived classifier and the effectiveness of selection at improving overall classifier performance, case selection can still be beneficial, regardless of the level of class imbalance.
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