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

Mohamed Ali Hadj Taieb, Mohamed Ben Aouicha
August 2021
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Abstract

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.

Type
Conference paper
Multi-Label Classification Confusion Matrix Evaluation Semantic Similarity Semantic Alignment Accuracy Rate
Mohamed Ali Hadj Taieb
Mohamed Ali Hadj Taieb
Assistant professor

My research interests include semantic similarity, semantic relatedness, knowledge representation, Big Data, social media, data management systems and graph embedding.

Mohamed Ben Aouicha
Mohamed Ben Aouicha
Professor

My research interests concern information retrieval, semantic technologies, social media analytics, knowledge representation, Big Data and graph embedding.