Effectively Capturing Label Correlation for Tabular Multi-Label Classification

verfasst von: Sajjad Kamali Siahroudi, Zahra Ahmadi, Daniel Kudenko
Abstract: Multi-label data is prevalent across various applications, where instances can be annotated with a set of classes. Although multi-label data can take various forms, such as images and text, tabular multi-label data stands out as the predominant data type in many real-world scenarios. Over the past decades, numerous methods have been proposed for tabular multi-label classification. Effectively addressing challenges like class imbalance, correlation among labels and features, and scalability is crucial for a high-performance multi-label classifier. However, many existing methods fall short of fully considering the correlation between labels and features. In cases where attempts are made, they often encounter high computational costs, rendering them impractical for large datasets. This paper in- troduces an innovative classification method for tabular multi-label data, utilizing a fusion of transformers and graph convolutional networks (GCN). The central concept of the proposed approach involves transforming tabular data into images, leveraging state-of-the-art methods in image processing, including image-based transformers and pre-trained models to capture correlation among labels effectively. Our approach jointly learns the representation of feature space and the correlation among labels within a unified network. To substantiate the performance of our proposed method, we conducted a rigorous series of experiments across diverse multi-label datasets1. The results underscore the superior performance and scalability of our approach compared to other existing state-of-the-art methods. This work not only contributes a novel perspective to the field of tabular multi-label classification but also showcases advancements in both accuracy and scalability.
Organisationseinheit(en): Forschungszentrum L3S
Typ: Aufsatz in Konferenzband
Seiten: 1060-1069
Anzahl der Seiten: 10
Publikationsdatum: 21.10.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Allgemeine Unternehmensführung und Buchhaltung, Allgemeine Entscheidungswissenschaften
Elektronische Version(en): https://doi.org/10.1145/3627673.3679772 (Zugang: Geschlossen)

BibTeX

@inproceedings{46481d21604a4fb0b0dd0c4cbf5cb195,
title = "Effectively Capturing Label Correlation for Tabular Multi-Label Classification",
abstract = "Multi-label data is prevalent across various applications, where instances can be annotated with a set of classes. Although multi-label data can take various forms, such as images and text, tabular multi-label data stands out as the predominant data type in many real-world scenarios. Over the past decades, numerous methods have been proposed for tabular multi-label classification. Effectively addressing challenges like class imbalance, correlation among labels and features, and scalability is crucial for a high-performance multi-label classifier. However, many existing methods fall short of fully considering the correlation between labels and features. In cases where attempts are made, they often encounter high computational costs, rendering them impractical for large datasets. This paper in- troduces an innovative classification method for tabular multi-label data, utilizing a fusion of transformers and graph convolutional networks (GCN). The central concept of the proposed approach involves transforming tabular data into images, leveraging state-of-the-art methods in image processing, including image-based transformers and pre-trained models to capture correlation among labels effectively. Our approach jointly learns the representation of feature space and the correlation among labels within a unified network. To substantiate the performance of our proposed method, we conducted a rigorous series of experiments across diverse multi-label datasets1. The results underscore the superior performance and scalability of our approach compared to other existing state-of-the-art methods. This work not only contributes a novel perspective to the field of tabular multi-label classification but also showcases advancements in both accuracy and scalability.",
keywords = "classification, graph convolutional network, multi-label, tabular data, transformer",
author = "{Kamali Siahroudi}, Sajjad and Zahra Ahmadi and Daniel Kudenko",
note = "Publisher Copyright: {\textcopyright} 2024 ACM.; 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024 ; Conference date: 21-10-2024 Through 25-10-2024",
year = "2024",
month = oct,
day = "21",
doi = "10.1145/3627673.3679772",
language = "English",
pages = "1060--1069",
booktitle = "CIKM 2024",
publisher = "Association for Computing Machinery",
}