Summary of Heterogeneous Random Forest, by Ye-eun Kim et al.
Heterogeneous Random Forest
by Ye-eun Kim, Seoung Yun Kim, Hyunjoong Kim
First submitted to arxiv on: 24 Oct 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 A novel approach to random forest (RF) classification problems is proposed, introducing heterogeneous RF (HRF). By deliberately injecting heterogeneity during tree construction, HRF enhances tree diversity and mitigates selection bias. This is achieved by assigning lower weights to features used for splitting near the root node of previous trees when constructing subsequent feature subspaces. Simulation studies demonstrate HRF’s effectiveness in increasing diversity and improving performance on datasets with fewer noise features. Comparative tests across 52 datasets, including real-world and synthetic data, show that HRF consistently outperforms other ensemble methods in terms of accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Random forest is a popular way to predict what something will be based on its characteristics. But sometimes, these predictions can be biased because the trees are too similar. A new approach called heterogeneous random forest tries to fix this by making each tree slightly different from the others. This makes the whole group of trees more diverse and better at predicting things correctly. Researchers tested this method on 52 different datasets and found that it usually worked better than other methods. |
Keywords
» Artificial intelligence » Classification » Random forest » Synthetic data
