Abstract The recognition of opaque minerals by polarized reflected-light microscopy is a challenging task due to the use of certain qualitative properties that may lead to ambiguities in their identification. The use of a decision tree may simplify and guide the evaluation of these properties for reaching a proper mineral identification. Improvements of such classification trees can contribute to employ fewer properties if the depth of the tree is reduced, and achieve less uncertainty in case of reducing the number of minerals in the terminal nodes. This work describes a proposal for obtaining precise and compact classification trees and its application in the optical identification of minerals. The method builds a decision tree by using machine learning techniques, after grouping the minerals with the same properties. Its classification performance was evaluated in a comparison with different classifiers. Also, the complexity of the resulting tree was compared to a widely used tree diagram. The results show that this method can generate classification diagrams that employ few properties and with a reduced number of minerals in each final group, so decreasing the uncertainty of the identification. Furthermore, the inclusion of an additional property (reflectance) was evaluated, applying it to data of common opaque and rock-forming minerals. The resulting tree presents an improvement in the identification, and without a significant increase in the number of properties needed to identify each mineral group. A web application has also been developed to interactively embody the classification process that follows the decision tree obtained.

中文翻译：

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分类树在提高常见不透明矿物光学识别中的应用

摘要 通过偏振反射光显微镜识别不透明矿物是一项具有挑战性的任务，因为某些定性属性的使用可能会导致其识别模糊。决策树的使用可以简化和指导对这些特性的评估，以实现正确的矿物识别。如果树的深度减少，这种分类树的改进可以有助于使用更少的属性，并且在减少终端节点中矿物数量的情况下实现更少的不确定性。这项工作描述了获得精确和紧凑分类树的建议及其在矿物光学识别中的应用。该方法在对具有相同属性的矿物进行分组后，使用机器学习技术构建决策树。通过与不同分类器的比较来评估其分类性能。此外，将生成的树的复杂性与广泛使用的树图进行了比较。结果表明，该方法可以生成使用较少属性且每个最终组中矿物数量减少的分类图，从而降低识别的不确定性。此外，还评估了附加属性（反射率）的包含情况，将其应用于常见不透明和岩石形成矿物的数据。生成的树显示了识别的改进，并且没有显着增加识别每个矿物组所需的属性数量。还开发了一个 Web 应用程序来交互式地体现遵循所获得的决策树的分类过程。