A Method for Discovering Novel Classes in Tabular Data

TL;DR

This paper introduces TabularNCD, a novel approach for discovering new classes in heterogeneous tabular data by leveraging known class information and multi-task learning, demonstrating effectiveness across multiple datasets.

Contribution

The paper proposes a new method, TabularNCD, for novel class discovery in tabular data, including a pseudo-labeling technique and a joint optimization framework.

Findings

Outperforms 3 competitors on 7 datasets

Effective in heterogeneous tabular data contexts

Extends NCD applicability beyond images

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. Extensive experiments are conducted to evaluate our method and demonstrate its effectiveness against 3 competitors on 7 diverse public classification datasets.