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.
Cite
@article{arxiv.2011.00109,
title = {Knowledge-Based Construction of Confusion Matrices for Multi-Label Classification Algorithms using Semantic Similarity Measures},
author = {Houcemeddine Turki and Mohamed Ali Hadj Taieb and Mohamed Ben Aouicha},
journal= {arXiv preprint arXiv:2011.00109},
year = {2021}
}
Comments
Camera-Ready for International Workshop on Data meets Applied Ontologies in Explainable AI (DAO-XAI 2021)