FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data

Wang, Huaduo; Gupta, Gopal

doi:10.1007/978-3-030-99461-7_13

Cited by 7 publications

(2 citation statements)

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“…It repeats this process to learn exceptions to exceptions, exceptions to exceptions to exceptions, and so on. The FOLD-R++ algorithm by Wang and Gupta (2022) is a new scalable ILP algorithm that builds upon the FOLD algorithm to deal with the efficiency and scalability issues of the FOLD and FOIL algorithms. It introduces the prefix sum computation and other optimizations to speed up the learning process while providing human-friendly explanation for its prediction using the s(CASP) answer set programming system (ASP) of Arias et al (2018).…”

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Shakerin²,

Gupta³

2022

Theory and Practice of Logic Programming

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FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for multi-category classification tasks while maintaining efficiency and scalability. The FOLD-RM algorithm is competitive in performance with the widely used, state-of-the-art algorithms such as XGBoost and multi-layer perceptrons, however, unlike these algorithms, the FOLD-RM algorithm produces an explainable model. FOLD-RM outperforms XGBoost on some datasets, particularly large ones. FOLD-RM also provides human-friendly explanations for predictions.

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Section: Introductionmentioning

confidence: 99%

“…The FOLD-R++ algorithmThe FOLD-R++ algorithm byWang and Gupta (2022) is a new ILP algorithm for binary classification that is built upon the FOLD algorithm ofShakerin et al (2017). Our FOLD-RM algorithm builds upon the FOLD-R++ algorithm.…”

mentioning

confidence: 99%