A SAT-based approach to learn explainable decision sets
From MaRDI portal
Publication:1799125
DOI10.1007/978-3-319-94205-6_41OpenAlexW2810136497MaRDI QIDQ1799125
Nina Narodytska, Alexey Ignatiev, Felipe Pereira, João P. Marques-Silva
Publication date: 18 October 2018
Full work available at URL: https://doi.org/10.1007/978-3-319-94205-6_41
Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)
Related Items (15)
Learning optimal decision trees using constraint programming ⋮ On Tackling Explanation Redundancy in Decision Trees ⋮ Mathematical optimization in classification and regression trees ⋮ Efficient Learning of Interpretable Classification Rules ⋮ Shattering inequalities for learning optimal decision trees ⋮ SAT-based optimal classification trees for non-binary data ⋮ Time and space complexity of deterministic and nondeterministic decision trees ⋮ On optimal regression trees to detect critical intervals for multivariate functional data ⋮ Solving hybrid Boolean constraints in continuous space via multilinear Fourier expansions ⋮ Unnamed Item ⋮ Synthesis of quantifier-free DNF sentences from inconsistent samples of strings with EF games and SAT ⋮ Synthesis of a DNF formula from a sample of strings using Ehrenfeucht-Fraïssé games ⋮ Interpretable machine learning: fundamental principles and 10 grand challenges ⋮ Learning Optimal Decision Sets and Lists with SAT ⋮ SAT-based rigorous explanations for decision lists
Uses Software
This page was built for publication: A SAT-based approach to learn explainable decision sets
