ISDA 2021 - How class generalization affects classical ML algorithms for mono-label classification
Channel: Houcemeddine Turki
Date: 07/11/2021

This recording highlights a paper entitled "How knowledge-driven class generalization affects classical machine learning algorithms for mono-label supervised classification" by Houcemeddine Turki, Mohamed Ali Hadj Taieb and Mohamed Ben Aouicha. The work explains how merging labels in mono-label classification to their parent category in a reference semantic resource can affect the accuracy of the classical machine learning models for supervised classification. It also advocates for the use of Wikimedia Commons (https://commons.wikimedia.org) as a large-scale dataset for image classification research. The paper is published as a part of the 21st International Conference on Intelligent Systems Design and Applications held online between 13 and 15 December 2021.