@inproceedings{
chen2024dofen,
title={{DOFEN}: Deep Oblivious Forest {EN}semble},
author={Kuan-Yu Chen and Ping-Han Chiang and Hsin-Rung Chou and Chih-Sheng Chen and Tien-Hao Chang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=umukvCdGI6}
}
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
DOFEN: Deep Oblivious Forest ENsemble
Kuan-Yu Chen Ping-Han Chiang Hsin-Rung Chou Chih-Sheng Chen Tien-Hao Chang