Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks.
Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks.
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机器学习应用和聚类方法优化改进了黑莓种质库中描述符的选择。
DOI：
10.3390/plants10020247
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发表时间：
2021-01-28
期刊：
影响因子：
--
通讯作者：
Ramírez-Gil JG
中科院分区：
文献类型：
--
作者：
Henao-Rojas JC;Rosero-Alpala MG;Ortiz-Muñoz C;Velásquez-Arroyo CE;Leon-Rueda WA;Ramírez-Gil JG
机器学习（ML）及其多种应用在改善对不同农业过程知识的解释方面具有比较优势。然而，存在阻碍适当使用的挑战，如可以在种质库的表型表征中看到的。本研究的目的是测试和优化不同的分析方法的基础上ML的优先级和选择悬钩子属植物的形态描述符。对26个基因型的55个描述符进行了评价，并确定了每个描述符的权重和区分能力。ML方法，如随机森林（RF），支持向量机，在线性和径向的形式，和神经网络进行了优化和比较。随后，用两种判别方法及其变体：层次凝聚聚类和K-均值对结果进行了验证。结果表明，RF呈现出最高的准确性（0.768）的方法评价，选择11个描述符的基础上的纯度（基尼指数），重要性，连接树的数量，和显着性（p值< 0.05）。此外，基于RF的优化描述符的K-means方法对悬钩子属植物具有更高的鉴别力，根据评估的统计数据。本研究提出了一个应用ML的植物种质库表征的特定形态变量的优化。
Machine learning (ML) and its multiple applications have comparative advantages for improving the interpretation of knowledge on different agricultural processes. However, there are challenges that impede proper usage, as can be seen in phenotypic characterizations of germplasm banks. The objective of this research was to test and optimize different analysis methods based on ML for the prioritization and selection of morphological descriptors of Rubus spp. 55 descriptors were evaluated in 26 genotypes and the weight of each one and its ability to discriminating capacity was determined. ML methods as random forest (RF), support vector machines, in the linear and radial forms, and neural networks were optimized and compared. Subsequently, the results were validated with two discriminating methods and their variants: hierarchical agglomerative clustering and K-means. The results indicated that RF presented the highest accuracy (0.768) of the methods evaluated, selecting 11 descriptors based on the purity (Gini index), importance, number of connected trees, and significance (p value < 0.05). Additionally, K-means method with optimized descriptors based on RF had greater discriminating power on Rubus spp., accessions according to evaluated statistics. This study presents one application of ML for the optimization of specific morphological variables for plant germplasm bank characterization.
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影响因子：
8.9
作者：
Huang, J;Ling, CX
通讯作者：
Ling, CX
影响因子：
3.7
作者：
Christodoulou MD;Battey NH;Culham A
通讯作者：
Culham A
影响因子：
5.8
作者：
Beck, Marcus W.
通讯作者：
Beck, Marcus W.
DOI：
10.1007/978-1-60327-241-4_13
发表时间：
2010-01-01
期刊：
DATA MINING TECHNIQUES FOR THE LIFE SCIENCES
影响因子：
--
作者：
Ben-Hur, Asa;Weston, Jason
通讯作者：
Weston, Jason
影响因子：
8
作者：
Bradley, AP
通讯作者：
Bradley, AP