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FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning
  Algorithm for Multi-Category Classification of Mixed Data
v1v2v3 (latest)

FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data

Theory and Practice of Logic Programming (TPLP), 2022
14 February 2022
Huaduo Wang
Farhad Shakerin
Gopal Gupta
ArXiv (abs)PDFHTML

Papers citing "FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data"

3 / 3 papers shown
Counterfactual Explanation Generation with s(CASP)
Counterfactual Explanation Generation with s(CASP)
Sopam Dasgupta
Farhad Shakerin
Joaquín Arias
Elmer Salazar
Gopal Gupta
LRM
316
1
0
23 Oct 2023
Logic-Based Explainability in Machine Learning
Logic-Based Explainability in Machine Learning
Sasha Rubin
LRMXAI
552
63
0
24 Oct 2022
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with
  Scalable Explainability
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable ExplainabilityInternational Symposium on Practical Aspects of Declarative Languages (PADL), 2022
Huaduo Wang
Gopal Gupta
253
19
0
16 Aug 2022
1
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