将表格数据转为雷达图信号，用卷积网络更好处理分类任务
How to RETIRE Tabular Data in Favor of Discrete Digital Signal Representation
- 把表格数据转成雷达图形式的离散数字信号
- 在分类准确率和计算复杂度上优于主流方法
- 提升模型可解释性与迁移能力，适合数据特征分析
深度神经网络在计算机视觉中的成功推动了多维编码（MDE）这一新研究方向的发展。这类方法旨在将表格数据转换为统一的离散数字信号（图像），以便对原本不适用的问题应用卷积网络。尽管已有若干工作出现，但多维编码方法仍较少，现有技术的应用范围也较窄。为此，本文提出雷达表征编码（RETIRE），将表格数据转化为雷达图，以捕捉每个实例的特征特性。在分类准确率和计算复杂度方面，RETIRE与当前最先进的MDE算法及XGBoost进行了对比。此外，还通过可迁移性和可解释性分析，深入探讨了RETIRE与现有MDE技术的性能表现。统计分析结果证实，RETIRE在各项指标上均显著优于现有方法。
原文摘要 · Abstract (English)
The successes achieved by deep neural networks in computer vision tasks have led in recent years to the emergence of a new research area dubbed Multi-Dimensional Encoding (MDE). Methods belonging to this family aim to transform tabular data into a homogeneous form of discrete digital signals (images) to apply convolutional networks to initially unsuitable problems. Despite the successive emerging works, the pool of multi-dimensional encoding methods is still low, and the scope of research on existing modality encoding techniques is quite limited. To contribute to this area of research, we propose the Radar-based Encoding from Tabular to Image REpresentation (RETIRE), which allows tabular data to be represented as radar graphs, capturing the feature characteristics of each problem instance. RETIRE was compared with a pool of state-of-the-art MDE algorithms as well as with XGBoost in terms of classification accuracy and computational complexity. In addition, an analysis was carried out regarding transferability and explainability to provide more insight into both RETIRE and existing MDE techniques. The results obtained, supported by statistical analysis, confirm the superiority of RETIRE over other established MDE methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。