基于ECOC支持向量机的智能模型验证方法

Yuchen Zhou, K. Fang, Mingpei Yang, P. Ma
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引用次数: 4

摘要

提出了一种基于纠错输出编码支持向量机(ECOC SVM)的智能模型验证方法。将计算机模型模拟时间序列与实际系统观测时间序列的相似性分析表述为一个多类分类问题。ECOC框架以通信理论的纠错原理为基础,将多类分类任务分解为多个二元分类问题。采用支持向量机作为基分类器,并采用一组相似度度量方法提取输入特征。与传统方法相比,本文提出的基于ECOC支持向量机的验证方法将多个相似测度整合为一个综合相似测度,并能从训练样本中学习预测可信度水平。应用结果表明,分类准确率达到82%,表明该方法适用于大型数据集的相似度分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Model Validation Method Based on ECOC SVM
This paper develops an intelligent model validation method based on error correcting output coding support vector machine (ECOC SVM). The similarity analysis between simulation time series from computerized model and observed time series from real-world system is formulated as a multi-class classification problem. The ECOC framework, built on the basis of the error correcting principles of communication theory, decomposes the multi-class classification task as multiple binary classification problems. The SVM is used as the base classifier and a set of similarity measure methods is applied to extract the input features. Compared to conventional methods, the proposed validation method based on ECOC SVM incorporates multiple similarity measures to a comprehensive similarity measure and can learn to predict the credibility level from training samples. The application result reveals that the classification accuracy achieved 82%, which means the proposed method is promising for the similarity analysis of large datasets.
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