标题： 基于语义相似性度量的知识构建多标签分类算法混淆矩阵 显示英文标题

标题： Knowledge-Based Construction of Confusion Matrices for Multi-Label Classification Algorithms using Semantic Similarity Measures

摘要： 到目前为止，多标签分类算法的评估使用了不考虑所考虑类别的语义的统计方法，并且完全依赖于贝叶斯推理等抽象计算。 目前，已经有一些尝试开发基于本体的方法，以更好地评估监督分类算法。 在本研究论文中，我们定义了一种新方法，该方法使用基于本体的特征语义相似性度量，在多标签分类中对齐预期标签与预测标签，并利用它来开发一种创建精确混淆矩阵的方法，以更有效地评估多标签分类算法。 显示英文摘要

摘要： So far, multi-label classification algorithms have been evaluated using statistical methods that do not consider the semantics of the considered classes and that fully depend on abstract computations such as Bayesian Reasoning. Currently, there are several attempts to develop ontology-based methods for a better assessment of supervised classification algorithms. In this research paper, we define a novel approach that aligns expected labels with predicted labels in multi-label classification using ontology-driven feature-based semantic similarity measures and we use it to develop a method for creating precise confusion matrices for a more effective evaluation of multi-label classification algorithms.