Summary of Dofen: Deep Oblivious Forest Ensemble, by Kuan-yu Chen et al.
DOFEN: Deep Oblivious Forest ENsemble
by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, Tien-Hao Chang
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed DOFEN (Deep Oblivious Forest Ensemble) architecture combines the strengths of Deep Neural Networks (DNNs) and Gradient Boosting Decision Trees (GBDT) to achieve state-of-the-art results on tabular data. Inspired by oblivious decision trees, DOFEN constructs relaxed oblivious decision trees (rODTs) through random condition combinations, followed by a two-level rODT forest ensembling process. This novel approach narrows the gap between DNNs and tree-based models on the Tabular Benchmark, which includes 73 datasets spanning various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DOFEN is a new way to use Deep Neural Networks (DNNs) for tabular data, like tables of numbers or words. Right now, other types of models called Gradient Boosting Decision Trees are better at this kind of data. But DOFEN tries to combine the best of both worlds. It makes special trees that don’t know what they’re looking for and then puts multiple trees together to make predictions. This helps DNNs do better on tabular data, making it more competitive with other models. |
Keywords
» Artificial intelligence » Boosting
