Identification of Critical-to-quality Characteristic in Complex Products Based on the Adaptive-Lasso Method

Wei Wang, Wenfeng Wang, Erqi Ding
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引用次数: 2

Abstract

Targeting the problem of the redundancy in complex product quality characteristics, the Adaptive-Lasso method is introduced into the identification of Critical-to-quality Characteristic. By using the Adaptive-Lasso method to filter variables, reduce the dimensions of the original quality data sample set, and obtain the order of the correlation between the quality Characteristics in the sample set and the quality category, the quality Characteristics with the highest classification correct ratio are selected to form the Critical-to-quality Characteristic subset. On this basis, the classification correct ratio of the selected Characteristic subset is tested by using the support vector machine. The example shows that compared with the traditional ReliefF method and Lasso method, this method can effectively remove the irrelevant and redundant features in the original data set to achieve the purpose of identifying the Critical-to-quality Characteristic.
基于自适应套索方法的复杂产品关键质量特征识别
针对复杂产品质量特征中存在的冗余问题,将自适应lasso方法引入到关键质量特征的识别中。通过Adaptive-Lasso方法对变量进行过滤,对原始质量数据样本集进行降维,得到样本集中质量特征与质量类别之间的相关阶数,选择分类正确率最高的质量特征组成Critical-to-quality特征子集。在此基础上,利用支持向量机测试所选特征子集的分类正确率。实例表明,与传统的ReliefF方法和Lasso方法相比,该方法可以有效地去除原始数据集中不相关和冗余的特征,达到识别临界质量特征的目的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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