样品(材料)
校准
选择(遗传算法)
计算机科学
聚类分析
样本量测定
数据挖掘
人工智能
机器学习
模式识别(心理学)
统计
数学
化学
色谱法
作者
Zhonghai He,Kun Shen,Xiaofang Zhang
标识
DOI:10.1177/09670335231211618
摘要
In multivariate calibration problems, model performance is affected significantly by the calibration samples used during model building. In recent years, active learning methods have become one of the best methods for sample selection. However, most active learning methods only select instances from prediction uncertainty or sample space distance, and these single-criteria methods tend to select undesired samples. In addition, sample density characterizes the spatial information carried by the sample, but few studies in quantitative analysis utilize sample density alone to select calibration samples. Considering these issues, based on the k-means clustering algorithm, this paper proposes an active learning sample selection method (Diversity Informativeness Density Active Learning, DIDAL), which combines the three criteria of diversity, informativeness and sample density. The most representative sample is iteratively selected for - addition to the calibration set for modeling and estimating the chemical concentration of analytes. Soybean meal and soy sauce samples were analyzed by DIDAL and compared with existing sample selection methods. The prediction results show that the DIDAL algorithm significantly outperforms several existing algorithms and is close to the performance of full-sample modeling. A model with high prediction accuracy can be constructed by selecting only a few samples using the DIDAL method.
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