A Decision Tree Based On Related Family

Decision trees are widely used supervised learning models known for their simplicity, interpretability, and effectiveness in classification and regression tasks. Feature selection can remove redundant and noisy features, enhancing the generalization and robustness of decision trees. However, due to the high computational cost of existing feature selection methods, it is typically applied only once before classifier training, providing the classifier with dimensionally reduced data. This limits the synergistic effect between feature selection and the construction of split nodes in decision trees. The Related Family is an efficient feature evaluation method proposed by our research team. Its efficiency allows us to use it in the construction of split nodes in decision trees, leading to better splitting criteria. Building on this method, We introduce the Dynamic Related Family Decision Tree DRFDT , which dynamically selects optimal features for each sample subgroup as the tree grows. Experiments demonstrate that DRFDT outperforms a wide range of classification algorithms across 15 UCI datasets, achieving an average accuracy of 89.30 . This represents significant improvements over classical single-feature decision tree methods CART: _3.87 , traditional classification algorithms KNN: _5.71 , SVM: _4.54 , multi-feature split decision tree algorithms CART-LC: _3.99 , O1: _4.25 , and state-of-the-art decision tree classification algorithms FGBDT: _4.88 , MPRBC: _4.77 , RSLRS: _26.84 .