D\'ecouvrir de nouvelles classes dans des donn\'ees tabulaires

• URL: http://arxiv.org/abs/2211.16352v1
• Date: Mon, 28 Nov 2022 09:48:55 GMT
• Title: D\'ecouvrir de nouvelles classes dans des donn\'ees tabulaires
• Authors: Colin Troisemaine, Joachim Flocon-Cholet, St\'ephane Gosselin, Sandrine Vaton, Alexandre Reiffers-Masson, Vincent Lemaire
• Abstract summary: In Novel Class Discovery (NCD), the goal is to find new classes in an unlabeled set given a labeled set of known but different classes. We show a way to extract knowledge from already known classes to guide the discovery process of novel classes in heterogeneous data.
• Score: 54.11148718494725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Novel Class Discovery (NCD), the goal is to find new classes in an unlabeled set given a labeled set of known but different classes. While NCD has recently gained attention from the community, no framework has yet been proposed for heterogeneous tabular data, despite being a very common representation of data. In this paper, we propose TabularNCD, a new method for discovering novel classes in tabular data. We show a way to extract knowledge from already known classes to guide the discovery process of novel classes in the context of tabular data which contains heterogeneous variables. A part of this process is done by a new method for defining pseudo labels, and we follow recent findings in Multi-Task Learning to optimize a joint objective function. Our method demonstrates that NCD is not only applicable to images but also to heterogeneous tabular data.

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