构建新型光滑支持向量机模型及其在心脏病诊断中的应用。
Construction of a new smooth support vector machine model and its application in heart disease diagnosis.
机构信息
School of Information, Beijing Wuzi University, Beijing, 101149, China.
School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China.
出版信息
PLoS One. 2023 Feb 9;18(2):e0280804. doi: 10.1371/journal.pone.0280804. eCollection 2023.
Support vector machine (SVM) is a new machine learning method developed from statistical learning theory. Since the objective function of the unconstrained SVM model is a non-smooth function, a lot of fast optimization algorithms can't be used to find the solution. Firstly, to overcome the non-smooth property of this model, a new padé33 approximation smooth function is constructed by rational approximation method, and a new smooth support vector machine model (SSVM) is established based on the smooth function. Then, by analyzing the performance of the smooth function, we find that the smooth precision is significantly higher than existing smooth functions. Moreover, theoretical and rigorous mathematical analyses are given to prove the convergence of the new model. Finally, it is applied to the heart disease diagnosis. The results show that the Padé33-SSVM model has better classification capability than existing SSVMs.
支持向量机（SVM）是一种从统计学习理论中发展出来的新机器学习方法。由于无约束 SVM 模型的目标函数是一个非光滑函数，因此无法使用大量快速优化算法来寻找解。首先，为了克服该模型的非光滑性，我们使用有理逼近方法构建了一个新的 Padé33 逼近光滑函数，并基于该光滑函数建立了一个新的光滑支持向量机模型（SSVM）。然后，通过分析光滑函数的性能，我们发现其光滑精度明显高于现有的光滑函数。此外，我们还进行了严格的理论和数学分析，以证明新模型的收敛性。最后，我们将其应用于心脏病诊断中。结果表明，Padé33-SSVM 模型的分类能力优于现有的 SSVM 模型。
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