An Interactive Interface for Novel Class Discovery in Tabular Data
Abstract
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.
Cite
Text
Troisemaine et al. "An Interactive Interface for Novel Class Discovery in Tabular Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023. doi:10.1007/978-3-031-43430-3_18Markdown
[Troisemaine et al. "An Interactive Interface for Novel Class Discovery in Tabular Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023.](https://mlanthology.org/ecmlpkdd/2023/troisemaine2023ecmlpkdd-interactive/) doi:10.1007/978-3-031-43430-3_18BibTeX
@inproceedings{troisemaine2023ecmlpkdd-interactive,
title = {{An Interactive Interface for Novel Class Discovery in Tabular Data}},
author = {Troisemaine, Colin and Flocon-Cholet, Joachim and Gosselin, Stéphane and Reiffers-Masson, Alexandre and Vaton, Sandrine and Lemaire, Vincent},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2023},
pages = {295-299},
doi = {10.1007/978-3-031-43430-3_18},
url = {https://mlanthology.org/ecmlpkdd/2023/troisemaine2023ecmlpkdd-interactive/}
}