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

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.

Houcemeddine Turki

Medical student

My research interests include the development of a large-scale framework for using open resources and semantic technologies for driving biomedical informatics and research evaluation at a low cost.

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

Associate professor

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