1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024
An Algorithmic Framework for Constructing Multiple Decision Trees by Evaluating Their Combination Performance Throughout the Construction Process
Keito Tajima, Naoki Ichijo, Yuta Nakahara +1
Predictions using a combination of decision trees are known to be effective in machine learning. Typical ideas for constructing a combination of decision trees for prediction are b…
stat.ML2024★ 1 cited
Boosting-Based Sequential Meta-Tree Ensemble Construction for Improved Decision Trees
Ryota Maniwa, Naoki Ichijo, Yuta Nakahara +1
A decision tree is one of the most popular approaches in machine learning fields. However, it suffers from the problem of overfitting caused by overly deepened trees. Then, a meta-…
cs.LG2023
Prediction Algorithms Achieving Bayesian Decision Theoretical Optimality Based on Decision Trees as Data Observation Processes
Yuta Nakahara, Shota Saito, Naoki Ichijo +2
In the field of decision trees, most previous studies have difficulty ensuring the statistical optimality of a prediction of new data and suffer from overfitting because trees are…