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The issue of class imbalance has been well-studied in the multi-class classification framework, but its exploration within multi-dimensional classification (MDC) has been somewhat overlooked due to the complexity of the imbalance shift phenomenon. This occurs when a sample is categorized as belonging to both a minor and a major class, depending on the labeling dimension.
Addressing this ga by Simon Mansfield
Sydney, Australia (SPX) Apr 24, 2024
The issue of class imbalance has been well-studied in the multi-class classification framework, but its exploration within multi-dimensional classification (MDC) has been somewhat overlooked due to the complexity of the imbalance shift phenomenon. This occurs when a sample is categorized as belonging to both a minor and a major class, depending on the labeling dimension.
Addressing this gap, De-Chuan Zhan and his team at LAMDA, Nanjing University, have introduced innovative research in the field of Multi-dimensional Classification, published in Frontiers of Computer Science, which is co-published by Higher Education Press and Springer Nature.
The researchers highlight the importance of considering dimension-wise metrics in real-world MDC scenarios and have introduced two specific metrics aimed at this. They also noted imbalanced class distributions within each labeling dimension and developed a novel Imbalance-Aware Fusion Model (IMAM) to tackle these challenges.
The IMAM approach initially separates the task into various multi-class classification challenges, each tailored to a specific dimension. This allows the development of imbalance-aware deep models that maintain robust performance across all dimensions without compromising on individual accuracy. Additionally, the IMAM method utilizes dimension-specific models to serve as multiple teachers, transferring their knowledge to a comprehensive student model that integrates all dimensions.
Comprehensive testing on diverse MDC datasets has shown that the IMAM method significantly outperforms existing models, bridging a considerable performance gap.
Research Report:Revisiting multi-dimensional classification from a dimension-wise perspective
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