论文标题
使用语义相似性度量的多标签分类算法的基于知识的混淆矩阵构造
Knowledge-Based Construction of Confusion Matrices for Multi-Label Classification Algorithms using Semantic Similarity Measures
论文作者
论文摘要
到目前为止，已经使用统计方法评估了多标签分类算法，这些算法不考虑所考虑类的语义，并且完全取决于诸如贝叶斯推理的抽象计算。当前，有几项尝试开发基于本体的方法来更好地评估监督分类算法。在本研究论文中，我们定义了一种新型方法，该方法将预期标签与使用基于本体驱动的特征的语义相似性测量指标在多标签分类中的预测标签保持一致，并且我们使用它来开发一种方法来创建精确的混淆矩阵，以更有效地评估多标签分类算法的更有效评估。
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.
