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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Combined Feature Selection and Cancer Prognosis Using Support Vector Machine Regression
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Combined Feature Selection and Cancer Prognosis Using Support Vector Machine Regression

机译：支持向量机回归的特征选择与癌症预后的结合

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摘要

Prognostic prediction is important in medical domain, because it can be used to select an appropriate treatment for a patient by predicting the patient's clinical outcomes. For high-dimensional data, a normal prognostic method undergoes two steps: feature selection and prognosis analysis. Recently, the L_1hbox{-}L_2-norm Support Vector Machine (L_1hbox{-}L_2 SVM) has been developed as an effective classification technique and shown good classification performance with automatic feature selection. In this paper, we extend L_1hbox{-}L_2 SVM for regression analysis with automatic feature selection. We further improve the L_1hbox{-}L_2 SVM for prognostic prediction by utilizing the information of censored data as constraints. We design an efficient solution to the new optimization problem. The proposed method is compared with other seven prognostic prediction methods on three real-world data sets. The experimental results show that the proposed method performs consistently better than the medium performance. It is more efficient than other algorithms with the similar performance.
机译：预后预测在医学领域很重要，因为它可以通过预测患者的临床结果来为患者选择合适的治疗方法。对于高维数据，正常的预后方法包括两个步骤：特征选择和预后分析。最近，L_1hbox {-} L_2范数支持向量机（L_1hbox {-} L_2 SVM）已被开发为一种有效的分类技术，并且在具有自动特征选择的情况下显示出良好的分类性能。在本文中，我们将L_1hbox {-} L_2 SVM扩展为具有自动特征选择功能的回归分析。通过使用审查数据的信息作为约束，我们进一步改进了L_1hbox {-} L_2 SVM以进行预后预测。我们针对新的优化问题设计了有效的解决方案。将所提出的方法与三个真实数据集上的其他七个预后预测方法进行了比较。实验结果表明，该方法的性能始终优于中等性能。它比具有类似性能的其他算法效率更高。

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