Optimal SVM parameter selection for non-separable and unbalanced datasets.
Optimal SVM parameter selection for non-separable and unbalanced datasets.
复制标题
针对不可分离和不平衡数据集的最佳 SVM 参数选择。
DOI：
10.1007/s00158-014-1105-z
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Chen,Zhao
中科院分区：
文献类型：
--
作者：
Jiang,Peng;Missoum,Samy;Chen,Zhao
本文提出了用于选择支持向量机（SVM）分类器在不可分离和不平衡数据集的情况下的最优参数的三个验证指标的研究。这种情况在实验或临床获得数据时经常遇到。在这项工作中选择的三个指标是ROC曲线下的面积（AUC），准确度和平衡准确度。这些验证指标仅使用计算数据进行测试，这使得创建完全可分离的数据集成为可能。通过这种方式，代表现实世界问题的不可分离数据集可以通过投影到较低维子空间来创建。可分离数据集的知识，在现实世界的问题中是未知的，提供了一个参考，使用称为“加权似然”的数量来比较三个验证指标。作为应用实例，研究了髋部骨折预测的分类模型。数据来自股骨的参数化有限元模型。在可分离性、不平衡比率和训练集大小的几个级别上，研究了各种验证度量的性能。
This article presents a study of three validation metrics used for the selection of optimal parameters of a support vector machine (SVM) classifier in the case of non-separable and unbalanced datasets. This situation is often encountered when the data is obtained experimentally or clinically. The three metrics selected in this work are the area under the ROC curve (AUC), accuracy, and balanced accuracy. These validation metrics are tested using computational data only, which enables the creation of fully separable sets of data. This way, non-separable datasets, representative of a real-world problem, can be created by projection onto a lower dimensional sub-space. The knowledge of the separable dataset, unknown in real-world problems, provides a reference to compare the three validation metrics using a quantity referred to as the “weighted likelihood”. As an application example, the study investigates a classification model for hip fracture prediction. The data is obtained from a parameterized finite element model of a femur. The performance of the various validation metrics is studied for several levels of separability, ratios of unbalance, and training set sizes.