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Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by
  Answer Set Programming

Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming

17 September 2021
A. Takemura
Katsumi Inoue
ArXiv (abs)PDFHTML

Papers citing "Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming"

5 / 5 papers shown
Title
Generating Global and Local Explanations for Tree-Ensemble Learning
  Methods by Answer Set Programming
Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming
A. Takemura
Katsumi Inoue
49
0
0
14 Oct 2024
Reason to explain: Interactive contrastive explanations (REASONX)
Reason to explain: Interactive contrastive explanations (REASONX)
Laura State
Salvatore Ruggieri
Franco Turini
LRM
100
1
0
29 May 2023
FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning
  Algorithm for Multi-Category Classification of Mixed Data
FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data
Huaduo Wang
Farhad Shakerin
Gopal Gupta
70
8
0
14 Feb 2022
FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default
  Theories from Mixed Data
FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data
Huaduo Wang
G. Gupta
126
14
0
15 Oct 2021
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
1.3K
17,197
0
16 Feb 2016
1