An Interactive Interface for Novel Class Discovery in Tabular Data

2023/06/22 by Colin Troisemaine, Joachim Flocon-Cholet, Troisemaine, Colin +9 · 1 citation
Computer Science · #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Imbalanced Data Classification Techniques #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2306.12919

openalex publication_date 2023/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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• Category Discovery: An Open-World Perspective

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