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IMLI: An Incremental Framework for MaxSAT-Based Learning of
  Interpretable Classification Rules

IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules

7 January 2020
Bishwamittra Ghosh
Kuldeep S. Meel
ArXivPDFHTML

Papers citing "IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules"

6 / 6 papers shown
Title
An Incremental MaxSAT-based Model to Learn Interpretable and Balanced
  Classification Rules
An Incremental MaxSAT-based Model to Learn Interpretable and Balanced Classification Rules
Antônio Carlos Souza Ferreira Júnior
Thiago Alves Rocha
18
0
0
25 Mar 2024
Logic-Based Explainability in Machine Learning
Logic-Based Explainability in Machine Learning
Sasha Rubin
LRM
XAI
57
39
0
24 Oct 2022
Eliminating The Impossible, Whatever Remains Must Be True
Eliminating The Impossible, Whatever Remains Must Be True
Jinqiang Yu
Alexey Ignatiev
Peter J. Stuckey
Nina Narodytska
Sasha Rubin
32
23
0
20 Jun 2022
Rule Generation for Classification: Scalability, Interpretability, and Fairness
Rule Generation for Classification: Scalability, Interpretability, and Fairness
Tabea E. Rober
Adia C. Lumadjeng
M. Akyuz
cS. .Ilker Birbil
43
2
0
21 Apr 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand
  Challenges
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Cynthia Rudin
Chaofan Chen
Zhi Chen
Haiyang Huang
Lesia Semenova
Chudi Zhong
FaML
AI4CE
LRM
59
655
0
20 Mar 2021
Computing Optimal Decision Sets with SAT
Computing Optimal Decision Sets with SAT
Jinqiang Yu
Alexey Ignatiev
Peter J. Stuckey
P. L. Bodic
FAtt
22
26
0
29 Jul 2020
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