Details
| Original language | English |
|---|---|
| Title of host publication | CIKM 2024 |
| Subtitle of host publication | Proceedings of the 33rd ACM International Conference on Information and Knowledge Management |
| Pages | 1060-1069 |
| Number of pages | 10 |
| ISBN (electronic) | 9798400704369 |
| Publication status | Published - 21 Oct 2024 |
| Event | 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024 - Boise, United States Duration: 21 Oct 2024 → 25 Oct 2024 |
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
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- General Business,Management and Accounting
- Decision Sciences(all)
- General Decision Sciences
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CIKM 2024 : Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 2024. p. 1060-1069.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Effectively Capturing Label Correlation for Tabular Multi-Label Classification
AU - Kamali Siahroudi, Sajjad
AU - Ahmadi, Zahra
AU - Kudenko, Daniel
N1 - Publisher Copyright: © 2024 ACM.
PY - 2024/10/21
Y1 - 2024/10/21
N2 - 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.
AB - 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.
KW - classification
KW - graph convolutional network
KW - multi-label
KW - tabular data
KW - transformer
UR - http://www.scopus.com/inward/record.url?scp=85209995349&partnerID=8YFLogxK
U2 - 10.1145/3627673.3679772
DO - 10.1145/3627673.3679772
M3 - Conference contribution
AN - SCOPUS:85209995349
SP - 1060
EP - 1069
BT - CIKM 2024
T2 - 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024
Y2 - 21 October 2024 through 25 October 2024
ER -