JUCS - Journal of Universal Computer Science 26(1): 71-88, doi: 10.3897/jucs.2020.005
Label Clustering for a Novel Problem Transformation in Multi-label Classification
expand article infoSmail Sellah, Vincent Hilaire
‡ Université Bourgogne Franche-Comté, Belfort, France
Open Access
Document classification is a large body of search, many approaches were proposed for single label and multi-label classification. We focus on the multi-label classification more precisely those methods that transformation multi-label classification into single label classification. In this paper, we propose a novel problem transformation that leverage label dependency. We used Reuters-21578 corpus that is among the most used for text categorization and classification research. Results show that our approach improves the document classification at least by 8% regarding one-vs-all classification.
classification, clustering, feature extraction, ontology