Deep Neural Networks (DNNs) have revolutionized artificial intelligence, achieving impressive results on diverse data types, including images, videos, and texts. However, DNNs still lag behind Gradient Boosting Decision Trees (GBDT) on tabular data, a format extensively utilized across various domains. This paper introduces DOFEN, which stands for Deep Oblivious Forest ENsemble. DOFEN is a novel DNN architecture inspired by oblivious decision trees and achieves on-off sparse selection of columns. DOFEN surpasses other DNNs on tabular data, achieving state-of-the-art performance on the well-recognized benchmark: Tabular Benchmark, which includes 73 total datasets spanning a wide array of domains. The code of DOFEN is available at: https://github.com/Sinopac-Digital-Technology-Division/DOFEN.

翻译：深度神经网络（DNNs）在人工智能领域引发了革命，在图像、视频和文本等多种数据类型上取得了令人瞩目的成果。然而，对于表格数据——一种在各领域广泛使用的数据格式，DNNs 的表现仍落后于梯度提升决策树（GBDT）。本文提出了 DOFEN（深度遗忘森林集成），这是一种受遗忘决策树启发的新型 DNN 架构，实现了对列的开-关稀疏选择。DOFEN 在表格数据上超越了其他 DNNs，在公认的基准测试——涵盖广泛领域共 73 个数据集的表格基准测试中，取得了最先进的性能。DOFEN 的代码可在以下网址获取：https://github.com/Sinopac-Digital-Technology-Division/DOFEN。