动态置信度值选择。实验研究

R. Burduk
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引用次数: 0

摘要

机器学习方法经常被用于开发有效的医疗决策支持系统。集成选择是数据挖掘的最新趋势之一。本文提出了一种针对二值分类任务的动态置信度选择算法。在实验中,我们使用支持向量机、k近邻、中立网络和决策树模型作为基本分类器。在多个公开的医学诊断数据集上的实验验证了该算法的有效性。结果表明,动态置信度选择优于所有基础学习模型构建的集成分类器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic confidence values selection — Experimental studies
The machine learning methods are often used in the development of the effective medical decision support systems. One of the latest trends in data mining is the ensemble selection. In this paper, we present the algorithm of the dynamic confidence values selection, which is dedicated to the binary classification task. In the experiment we use Support Vector Machine, k-Nearest-Neighbors, Neutral Network and Decision Trees models as base classifiers. Experiments on several publicly available medical diagnosis data sets verify the effectiveness of the proposed algorithm. The results demonstrate that the dynamic confidence values selection outperforms the ensemble classifier built with all base learning models.
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