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首页> 外文会议>International Symposium on Computer and Information Sciences(ISCIS 2004); 20041027-29; Kemer-Antalya(TR) >The Effects of Data Properties on Local, Piecewise, Global, Mixture of Experts, and Boundary-Optimized Classifiers for Medical Decision Making
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The Effects of Data Properties on Local, Piecewise, Global, Mixture of Experts, and Boundary-Optimized Classifiers for Medical Decision Making

机译：数据属性对专家决策的局部，分段，全局，专家混合和边界优化分类器的影响

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摘要

This paper investigates the issues of data properties with various local, piecewise, global, mixture of experts (ME) and boundary-optimized classifiers in medical decision making cases. A local k-nearest neighbor (k-NN), piecewise decision tree C4.5 and CART algorithms, global multilayer perceptron (MLP), mixture of experts (ME) algorithm based on normalized radial basis function (RBF) net and boundary-optimized support vector machines (SVM) algorithm are applied to three cases with different data sizes: A stroke risk factors discrimination case with a small data size N, an antenatal hypoxia discrimination case with a medium data size N and an intranatal hypoxia monitoring case with a reasonably large data size individual classification cases. Normalized RBF, MLP classifiers give good results in the studied decision making cases. The parameter setting of SVM is adjustable to various receiver operating characteristics (ROC).
机译：本文使用医疗决策案例中的各种局部，分段，全局，专家（ME）和边界优化分类器来研究数据属性问题。局部k最近邻（k-NN），分段决策树C4.5和CART算法，全局多层感知器（MLP），基于归一化径向基函数（RBF）网络和边界优化的专家混合（ME）算法支持向量机（SVM）算法适用于三种数据大小不同的案例：数据大小为N的中风危险因素辨别案例，数据大小为N的产前低氧辨别案例和合理的出生时低氧监测案例大数据量的个别分类案例。在研究的决策案例中，归一化的RBF，MLP分类器可提供良好的结果。 SVM的参数设置可根据各种接收器工作特性（ROC）进行调整。

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