Authors: Keito Tajima, Naoki Ichijo, Yuta Nakahara, Toshiyasu Matsushima

Published on: February 09, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.06452

Summary

• What is new: Introducing a novel framework for constructing and evaluating combinations of decision trees simultaneously throughout the model building process.
• Why this is important: Existing methods like bagging and boosting do not directly construct or evaluate a combination of decision trees for final predictions.
• What the research proposes: The proposed framework constructs new candidate combinations and evaluates their performance simultaneously, allowing for the selection of the most effective combination.
• Results: The framework demonstrated superior performance in experiments conducted on both synthetic and benchmark data.

Technical Details

Technological frameworks used: A new algorithmic framework for constructing and evaluating combinations of decision trees

Models used: Decision trees

Data used: Synthetic and benchmark data

Potential Impact

This research could disrupt markets heavily reliant on predictive modeling, such as finance, healthcare, and retail, offering companies in these sectors a more effective tool for prediction.

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