In recent times, machine learning-based methods have gained popularity in various materials science applications including microstructure image classification. This paper explores the use of classifier combination approaches for classifying the microstructure images with an improved accuracy. Classifier combination methods have been recognized as a state-of-the-art approach to enhance the performance of many challenging image classification tasks. Ensemble methods are used to increase the predictive performance of a learning system by combining the predictive performances of several base learners. In our proposed model, the features of three-class microstructural images are extracted using the rotational local tetra pattern feature descriptor. These features are separately fed to three different classifiers, namely support vector machine, random forest, and K nearest neighbor. Then, a classifier combination approach based on the confidence scores provided by these classifiers using fuzzy measures and fuzzy integrals is applied for the image recognition purpose. Unlike other straightforward classical classifier combination methods, this method nonlinearly aggregates the objective evidences in terms of a fuzzy membership function, with the subjective assessments of the relative importance of different classifiers. The proposed method has also been compared with many standard classifier combination approaches commonly found in the literature. The experimental results support the effectiveness of fuzzy combination to produce higher classification accuracy than that of the best base classifiers and some popular classifier combination methods.

近年来，基于机器学习的方法已在包括微观结构图像分类在内的各种材料科学应用中得到普及。本文探讨了使用分类器组合方法对微观结构图像进行分类的方法，以提高准确性。分类器组合方法已被公认为是一种先进的方法，可以增强许多具有挑战性的图像分类任务的性能。集成方法用于通过组合几个基础学习者的预测性能来提高学习系统的预测性能。在我们提出的模型中，使用旋转局部四图案特征描述符提取三类微结构图像的特征。这些功能分别提供给三个不同的分类器，即支持向量机，随机森林，K最近邻。然后，基于这些分类器使用模糊测度和模糊积分提供的置信度得分的分类器组合方法被应用于图像识别。与其他简单的经典分类器组合方法不同，该方法根据模糊隶属函数对客观证据进行非线性汇总，并对不同分类器的相对重要性进行主观评估。所提出的方法也已与文献中常见的许多标准分类器组合方法进行了比较。实验结果支持了模糊组合的有效性，该模糊组合产生的分类精度高于最佳基本分类器和一些流行的分类器组合方法。基于这些分类器使用模糊测度和模糊积分提供的置信度得分的分类器组合方法被应用于图像识别。与其他简单的经典分类器组合方法不同，该方法根据模糊隶属函数对客观证据进行非线性汇总，并对不同分类器的相对重要性进行主观评估。所提出的方法也已与文献中常见的许多标准分类器组合方法进行了比较。实验结果支持了模糊组合的有效性，该模糊组合产生的分类精度高于最佳基本分类器和一些流行的分类器组合方法。基于这些分类器使用模糊测度和模糊积分提供的置信度得分的分类器组合方法被应用于图像识别。与其他简单的经典分类器组合方法不同，该方法根据模糊隶属函数对客观证据进行非线性汇总，并对不同分类器的相对重要性进行主观评估。所提出的方法也已与文献中常见的许多标准分类器组合方法进行了比较。实验结果支持了模糊组合的有效性，该模糊组合产生的分类精度高于最佳基本分类器和一些流行的分类器组合方法。