• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Spectroscopy: Society for Applied Spectroscopy >Sample Consensus Model and Unsupervised Variable Consensus Model for Improving the Accuracy of a Calibration Model
【24h】

Sample Consensus Model and Unsupervised Variable Consensus Model for Improving the Accuracy of a Calibration Model

机译：采样共识模型和无监督的可变共识模型，用于提高校准模型的准确性

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In the quantitative analysis of spectral data, small sample size and high dimensionality of spectral variables often lead to poor accuracy of a calibration model. We proposed two methods, namely sample consensus and unsupervised variable consensus models, in order to solve the problem of poor accuracy. Three public near-infrared (NIR) or infrared (IR) spectroscopy data from corn, wine, and soil were used to build the partial least squares regression (PLSR) model. Then, Monte Carlo sampling and unsupervised variable clustering methods of a self-organizing map were coupled with the consensus modeling strategy to establish the multiple sub-models. Finally, sample consensus and unsupervised variable consensus models were obtained by assigning the weights to each PLSR sub-model. The calculated results show that both sample consensus and unsupervised variable consensus models can significantly improve the accuracy of the calibration model compared to the single PLSR model. The effectiveness of these two methods points out a new approach to achieve a further accurate result, which can take full advantage of the sample information and valid variable information.
机译：在光谱数据的定量分析中，光谱变量的小样本大小和高维度通常导致校准模型的准确性差。我们提出了两种方法，即采样共识和无监督的可变共识模型，以解决准确性差的问题。来自玉米，葡萄酒和土壤的三个公共近红外（NIR）或红外（IR）光谱数据，用于构建偏最小二乘回归（PLSR）模型。然后，与自组织地图的蒙特卡罗采样和无监督的可变聚类方法与共识建模策略相结合，以建立多个子模型。最后，通过将权重分配给每个PLSR子模型来获得样本共识和无监督的可变共识模型。计算结果表明，与单个PLSR模型相比，样本共识和无监督的可变共识模型可以显着提高校准模型的准确性。这两种方法的有效性指出了一种实现进一步准确结果的新方法，可以充分利用样本信息和有效的变量信息。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号