On sparse optimal regression trees

DOI10.1016/j.ejor.2021.12.022zbMath1495.62049OpenAlexW4200252157MaRDI QIDQ2670540

Emilio Carrizosa, Rafael Blanquero, Dolores Romero Morales, Cristina Molero-Río

Publication date: 11 March 2022

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2021.12.022

zbMATH Keywords

nonlinear programmingmachine learningsparsityclassification and regression treesoptimal regression trees

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of mathematical programming (90C90) Nonlinear programming (90C30) Learning and adaptive systems in artificial intelligence (68T05) Credit risk (91G40)

Related Items (2)

On multivariate randomized classification trees: \(l_0\)-based sparsity, VC dimension and decomposition methods ⋮ On optimal regression trees to detect critical intervals for multivariate functional data

Uses Software

• UCI-ml
• SciPy
• rpart
• glmnet
• randomForest
• Python
• shap
• DFVLR-SQP

Cites Work

• Supersparse linear integer models for optimized medical scoring systems
• Mathematical optimization in classification and regression trees
• Constructing optimal binary decision trees is NP-complete
• Hedonic housing prices and the demand for clean air
• Bi-criteria optimization of decision trees with applications to data analysis
• Learning decision trees with flexible constraints and objectives using integer optimization
• A cost-sensitive constrained Lasso
• Optimal decision trees for categorical data via integer programming
• Column generation based heuristic for learning classification trees
• Sparsity in optimal randomized classification trees
• A random forest guided tour
• Comprehensible credit scoring models using rule extraction from support vector machines
• Optimal randomized classification trees
• Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation
• Random forests
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