Progressive Feature Upgrade in Semi-supervised Learning on Tabular Domain

TL;DR

This paper introduces a novel semi-supervised learning framework for tabular data that employs conditional probability representation and progressive feature upgrading, addressing challenges posed by mixed data types and high-cardinality categorical features.

Contribution

It proposes a new framework combining conditional probability and progressive feature upgrading to improve semi-supervised learning on complex tabular datasets.

Findings

Outperforms existing methods on various tabular datasets.

Effective for high-cardinality categorical data.

Shows potential in semi-supervised applications.

Abstract

Recent semi-supervised and self-supervised methods have shown great success in the image and text domain by utilizing augmentation techniques. Despite such success, it is not easy to transfer this success to tabular domains. It is not easy to adapt domain-specific transformations from image and language to tabular data due to mixing of different data types (continuous data and categorical data) in the tabular domain. There are a few semi-supervised works on the tabular domain that have focused on proposing new augmentation techniques for tabular data. These approaches may have shown some improvement on datasets with low-cardinality in categorical data. However, the fundamental challenges have not been tackled. The proposed methods either do not apply to datasets with high-cardinality or do not use an efficient encoding of categorical data. We propose using conditional probability representation and an efficient progressively feature upgrading framework to effectively learn representations for tabular data in semi-supervised applications. The extensive experiments show superior performance of the proposed framework and the potential application in semi-supervised settings.