Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking
作者: Shourav B. Rabbani, Ivan V. Medri, Manar D. Samad
分类: cs.LG
发布日期: 2024-01-08
💡 一句话要点
提出数据中心基准以评估表格数据的注意力与对比学习
🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)
关键词: 表格数据 深度学习 机器学习 注意力机制 对比学习 基准测试 数据中心 性能评估
📋 核心要点
- 现有的深度学习方法在表格数据上未能显著超越传统机器学习，存在性能差距。
- 论文提出了一种数据中心的基准测试方法，评估注意力和对比学习在表格数据集上的表现。
- 实验结果显示，传统机器学习在易分类数据集上表现更好，而对比学习在高维数据上更具优势。
📝 摘要（中文）
尽管深度学习在图像和文本学习中取得了突破性成功，但在表格数据上与传统机器学习相比，进展有限。这一性能差距凸显了对学习算法进行数据中心处理和基准测试的必要性。本文对28个表格数据集（14个易分类和14个难分类）进行了广泛评估，比较了最先进的注意力和对比学习方法与传统深度学习和机器学习的表现。研究表明，传统机器学习在易分类数据集上表现优越，而在高维数据上，对比学习则占据优势。本文首次对多样化的表格数据集进行了统计分析，推动了该领域的进一步发展。
🔬 方法详解
问题定义：本文旨在解决深度学习在表格数据上表现不佳的问题，现有方法在不同数据集上的评估结果存在偏差。
核心思路：通过对28个表格数据集进行全面评估，比较注意力和对比学习方法与传统机器学习的效果，提出数据中心的基准测试。
技术框架：研究设计了一个包含多个阶段的评估框架，首先选择数据集，然后应用不同的学习算法，最后进行性能比较和统计分析。
关键创新：首次系统性地对注意力和对比学习在多样化表格数据集上的表现进行基准测试，揭示了不同算法在不同数据集上的适用性。
关键设计：在实验中，采用了多种损失函数和网络结构，特别关注样本间和特征间的注意力机制，以提升模型在复杂数据集上的表现。
🖼️ 关键图片
📊 实验亮点
实验结果显示，结合样本间和特征间的注意力机制在难分类数据集上显著优于传统机器学习方法，而在易分类数据集上，传统方法则表现更佳。对比学习在高维数据集上表现出色，提供了新的研究方向。
🎯 应用场景
该研究的潜在应用领域包括金融、医疗和市场分析等需要处理表格数据的行业。通过优化学习算法的选择，可以提高数据分析的准确性和效率，推动相关领域的智能化发展。
📄 摘要（原文）
Despite groundbreaking success in image and text learning, deep learning has not achieved significant improvements against traditional machine learning (ML) when it comes to tabular data. This performance gap underscores the need for data-centric treatment and benchmarking of learning algorithms. Recently, attention and contrastive learning breakthroughs have shifted computer vision and natural language processing paradigms. However, the effectiveness of these advanced deep models on tabular data is sparsely studied using a few data sets with very large sample sizes, reporting mixed findings after benchmarking against a limited number of baselines. We argue that the heterogeneity of tabular data sets and selective baselines in the literature can bias the benchmarking outcomes. This article extensively evaluates state-of-the-art attention and contrastive learning methods on a wide selection of 28 tabular data sets (14 easy and 14 hard-to-classify) against traditional deep and machine learning. Our data-centric benchmarking demonstrates when traditional ML is preferred over deep learning and vice versa because no best learning method exists for all tabular data sets. Combining between-sample and between-feature attentions conquers the invincible traditional ML on tabular data sets by a significant margin but fails on high dimensional data, where contrastive learning takes a robust lead. While a hybrid attention-contrastive learning strategy mostly wins on hard-to-classify data sets, traditional methods are frequently superior on easy-to-classify data sets with presumably simpler decision boundaries. To the best of our knowledge, this is the first benchmarking paper with statistical analyses of attention and contrastive learning performances on a diverse selection of tabular data sets against traditional deep and machine learning baselines to facilitate further advances in this field.