2021/01/27 by Jinxiong Zhang, Zhang, Jinxiong
Computer Science · #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Neural Networks and Applications #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.2101.11347
openalex publication_date 2021/01/27 · openalex created_date 2021/02/01 · openalex updated_date 2026/07/28
The decision tree recursively partitions the input space into regions and derives axis-aligned decision boundaries from data. Despite its simplicity and interpretability, decision trees lack parameterized representation, which makes it prone to overfitting and difficult to find the optimal structure. We propose Decision Machines, which embed Boolean tests into a binary vector space and represent the tree structure as a matrices, enabling an interleaved traversal of decision trees through matrix computation. Furthermore, we explore the congruence of decision trees and attention mechanisms, opening new avenues for optimizing decision trees and potentially enhancing their predictive power.