A Novel Random Forest Variant Based on Intervention Correlation Ratio
Tao Zhang, Tao Li, Zaifa Xue, Xin Lu, Le Gao · IEEE Transactions on Emerging Topics in Computational Intelligence · 2024
Random forest (RF) is a classical machine learning model, and many variants have been proposed to improve the performance or interpretability in recent years. To improve the classification performance and interpretability of RF under the premise of consistency, a novel RF variant named intervention correlation ratio random forest (ICR2F) is proposed. First, intervention correlation ratio (ICR) is proposed as a novel causality evaluation method by the ratio of pre- and post intervention on features which is used to select features and thresholds to divide a non-leaf node when building a decision tree. And then, decision trees are built based on ICR to construct ICR2F through ensemble learning. In addition, ICR2F is proven to satisfy consistency in exploring random forest in theory. Finally, experimental results on 20 UCI datasets have shown that ICR2F surpasses classical classifiers and the latest RF variants in classification performance under the premise of consistency and interpretability.