基于主动半监督回归的近红外光谱扩展方法.
Yican Huang1, Zhengguang Chen1, Jinming Liu1
1College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, 163319, China.
Analytica chimica acta
|July 19, 2024
概括
一个新的Safer Active半监督样本增强学习模型 (Safer-AS3A) 通过扩展有限的训练数据来增强近红外 (NIR) 光谱分析. 这种方法提高了模型的准确性和稳定性,克服了农业和制药应用中的挑战.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 机器学习 机器学习
背景情况:
- 近红外 (NIR) 光谱在农业和制药领域至关重要.
- 由于劳累的样本采集,培训数据有限,阻碍了NIR模型的优化.
- 现有的模型与小的,标记的数据集作斗争.
研究的目的:
- 为了应对在NIR光谱分析中有限的培训数据的挑战.
- 开发一种新型模型,有效地扩展样本数据集.
- 提高NIR频谱分析模型的准确性和实际应用.
主要方法:
- 积极学习 (AL) 和半监督学习 (SSL) 技术的整合.
- 开发更安全的积极的半监督样本增强学习模型 (Safer-AS3A).
- 使用AL,SSL和联合培训来生成高质量的伪标签.
主要成果:
- 更安全的AS3A在精度和稳定性方面优于可比的模型,使用有限的标记样品.
- 有效地扩展了NIR光谱数据集,提高了模型性能.
- 在柴油和射击数据集上的PLSR,BRR,SVR和RR模型中观察到显著的R2改进.
结论:
- 更安全的AS3A模型有效地扩展了NIR光谱数据集.
- 这种方法大大提高了NIR光谱分析的性能.
- 提供了NIR分析效率,精度和样本多样化的新途径.
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