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

• URL: http://arxiv.org/abs/2202.06913v1
• Date: Mon, 14 Feb 2022 18:07:54 GMT
• Title: FOLD-RM: A Scalable and Efficient Inductive Learning Algorithm for Multi-Category Classification of Mixed Data
• Authors: Huaduo Wang and Gopal Gupta
• Abstract summary: 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.
• Score: 3.1981440103815717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 XGBoost algorithm, however, unlike XGBoost, 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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