Summary of An Algorithmic Framework For Constructing Multiple Decision Trees by Evaluating Their Combination Performance Throughout the Construction Process, By Keito Tajima et al.
An Algorithmic Framework for Constructing Multiple Decision Trees by Evaluating Their Combination Performance Throughout the Construction Process
by Keito Tajima, Naoki Ichijo, Yuta Nakahara, Toshiyasu Matsushima
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes a novel algorithmic framework for constructing decision trees that simultaneously evaluates their combination performance throughout the construction process. The framework is based on repeating two procedures: first, it constructs new candidates of combinations of decision trees to find a proper combination; second, it evaluates each combination’s performance under certain criteria and selects a better one. This approach differs from traditional methods like bagging and boosting, which do not directly construct or evaluate the combination of decision trees. The authors demonstrate the effectiveness of their framework through experiments on synthetic and benchmark data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a new way to make predictions using decision trees in machine learning. Usually, we use techniques called bagging and boosting to combine decision trees, but they don’t consider how well the combination will work until the very end. The researchers came up with an idea to build and test different combinations of decision trees as you go along. They tested this approach on some fake data and real-world datasets to see if it works better than the usual methods. |
Keywords
* Artificial intelligence * Bagging * Boosting * Machine learning
