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首页> 外文期刊>Computational Materials Science >Use of decision tree models based on evolutionary algorithms for the morphological classification of reinforcing nano-particle aggregates
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Use of decision tree models based on evolutionary algorithms for the morphological classification of reinforcing nano-particle aggregates

机译：基于进化算法的决策树模型对增强纳米颗粒聚集体的形态分类

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An original method is presented to classify nano-particle aggregates into one of the morphological classes that were previously proposed in the literature. Carbon black was selected as a study case in this work, since it is a nanoreinforcement used massively in many industries, but the proposed method can be applied to other particulate aggregates as well. This methodology consists of three steps: (i) transmission electron microscopy image processing to compute a set of twenty-one morphological characteristics for each aggregate including the new attributes proposed herein, (ii) a multivariate analysis of the dataset to reduce the problem dimensionality and (iii) creation and assessment of decision trees based on evolutionary algorithms to classify the aggregates. The effectiveness of the method was proven for a balanced-per-class sample of forty-eight selected aggregates. The original dimension of the problem is reduced to three principal components, which explain 90% of the total variance. The best model classifies an aggregate into one of the four morphological classes in a simple comparison process. The accuracy of the applied model to classify new aggregates was 75%.
机译：提出了一种原始方法以将纳米粒子聚集体分类为文献中先前提出的形态学类别。在这项工作中选择了炭黑作为研究案例，因为它是在许多行业中批量使用的纳米因素，但也可以应用于其他颗粒聚集体。该方法包括三个步骤：（i）透射电子显微镜图像处理，用于计算每个聚合的一组二十一体形态特征，包括本文所提出的新属性，（ii）数据集的多变量分析，以减少问题维度和问题（iii）基于进化算法的决策树创建和评估分类聚合。该方法的有效性被证明是针对每阶级的四十八个选定聚集体的平衡样本。问题的原始维度减少到三个主要成分，这解释了总方差的90％。最好的模型将聚合分类为一个简单的比较过程中的四个形态类之一。应用模型对新聚集体进行分类的准确性为75％。

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