An Interactive Interface for Novel Class Discovery in Tabular Data

Colin Troisemaine (1, 2, 3) , Joachim Flocon-Cholet (2) , Stéphane Gosselin (2) , Alexandre Reiffers-Masson (1, 3) , Sandrine Vaton (1, 3) , Vincent Lemaire (2)

1 IMT Atlantique - INFO - Département Informatique
2 Orange Labs [Lannion]
3 Lab-STICC_MATHNET - Equipe Math & Net

Novel Class Discovery (NCD) is the problem of trying to discover novel classes in an unlabeled set, given a labeled set of different but related classes. The majority of NCD methods proposed so far only deal with image data, despite tabular data being among the most widely used type of data in practical applications. To interpret the results of clustering or NCD algorithms, data scientists need to understand the domain- and application-specific attributes of tabular data. This task is difficult and can often only be performed by a domain expert. Therefore, this interface allows a domain expert to easily run state-of-the-art algorithms for NCD in tabular data. With minimal knowledge in data science, interpretable results can be generated.

Keywords: novel class discovery; clustering; transfer learning; open world learning

Domains: Artificial Intelligence [cs.AI]

Colin Troisemaine, Joachim Flocon-Cholet, Stéphane Gosselin, Alexandre Reiffers-Masson, Sandrine Vaton, et al.. An Interactive Interface for Novel Class Discovery in Tabular Data. ECML PKDD 2023: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2023, Turin, Italy. pp.295-299, ⟨10.1007/978-3-031-43430-3_18⟩. ⟨hal-04158855⟩