基于K均值聚类和多核SVM的微钙化簇检测

Microcalcification Detection Based on K-means Cluster and Multiple Kernel Support Vector Machine

考虑到乳腺微钙化簇样本分布不平衡以及特征的多样性,提出了基于K均值聚类的多核支持向量机。即首先将训练样本聚合成K类,对每类样本加不同的惩罚因子,以平衡样本分布不平衡。其次针对样本特征多样性,将核函数做组合,得到多核支持向量分类器。使用主动反馈学习的方法来得到稳定的训练样本。实验结果表明,本方法与单核SVM及多核SVM相比,检对率至少可以提高两个百分点。

Considering the unbalanced distribution of the training samples and the multiformity of the features. A multiple kernel SVM based on K-means cluster algorithm was proposed. Firstly, training samples was clustered into K classes, different penalty factors were used for each class in order to balance the contributions of each class. Secondly, the multiple kernel support vector machine was proposed for diversity of the features. The stabilized training sample was obtained via active feedback learning. The result show that the detection rate can be improved at least 2 percent by the proposed method, compared with the single kernel SVM and the multiple kernel SVM.