Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for getting a better accuracy of the bughole classification. A texture feature deriving from the Gabor filter and gray-level run lengths is extracted in concrete surface images. Interpretable variables, which are also the components of the feature, are selected according to a presented cumulative voting strategy. An ensemble classifier with its base classifier automatically assigned is provided to detect whether a surface void exists in an image or not. Experimental results on 1000 image samples indicate the effectiveness of our method with a comparable prediction accuracy and model explicable.

中文翻译：

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从图像纹理特征中进行变量选择以自动分类混凝土表面空隙

机器学习在计算智能中起着重要作用，并已广泛应用于许多工程领域。在浇铸过程之后，混凝土表面上经常出现的表面空洞或小孔使相应的人工检查变得既费时，成本高，劳动强度大又不一致。为了更好地检查混凝土表面，需要对混凝土漏洞进行自动分类。本文提出了一种变量选择策略来追求特征的可解释性，并提出了一种自动集成分类方法，以提高漏洞分类的准确性。在混凝土表面图像中提取源自Gabor滤波器和灰度游程长度的纹理特征。可解释变量，也是要素的组成部分，根据提出的累积投票策略选择。提供了具有自动分配的基础分类器的整体分类器，以检测图像中是否存在表面空隙。在1000个图像样本上的实验结果表明，我们的方法具有相当的预测精度和可解释的模型，是有效的。