SVM and SVM Ensembles in Breast Cancer Prediction.
SVM and SVM Ensembles in Breast Cancer Prediction.
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DOI：
10.1371/journal.pone.0161501
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Tsai CF
中科院分区：
文献类型：
--
作者：
Huang MW;Chen CW;Lin WC;Ke SW;Tsai CF
乳腺癌是一种非常常见的女性疾病，如何有效地预测它是一个活跃的研究问题。许多统计和机器学习技术已被用于开发各种乳腺癌预测模型。其中，支持向量机（SVM）已被证明优于许多相关技术。构造SVM分类器首先需要确定核函数，不同的核函数会导致不同的预测性能。然而，很少有研究集中在检查基于不同的核函数的支持向量机的预测性能。此外，它是未知的SVM分类器集成，已被提出来提高单个分类器的性能，可以优于单个SVM分类器在乳腺癌预测方面。因此，本文的目的是充分评估SVM和SVM集成在小型和大型乳腺癌数据集上的预测性能。分类精度，ROC，F-措施，和训练SVM和SVM集成的计算时间进行了比较。实验结果表明，对于需要在数据预处理阶段进行特征选择的小规模数据集，基于Bagging方法的线性核SVM集成和基于Boosting方法的RBF核SVM集成是较好的选择。对于大规模数据集，基于提升的RBF核SVM集成比其他分类器性能更好。
Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.
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影响因子：
8.5
作者：
Huang, Cheng-Lung;Liao, Hung-Chang;Chen, Mu-Chen
通讯作者：
Chen, Mu-Chen
DOI：
10.1142/s0218001403002460
发表时间：
2003-05-01
影响因子：
1.5
作者：
Byun, H;Lee, SW
通讯作者：
Lee, SW
影响因子：
7.5
作者：
Breiman, L
通讯作者：
Breiman, L
DOI：
10.1109/34.667881
发表时间：
1998-03-01
影响因子：
23.6
作者：
Kittler, J;Hatef, M;Matas, J
通讯作者：
Matas, J
影响因子：
3.9
作者：
Frosyniotis, D;Stafylopatis, A;Likas, A
通讯作者：
Likas, A