Fold Rm

FOLD-RM represents a family of explainable machine learning algorithms designed for efficient and scalable classification tasks, particularly with mixed data types. Current research focuses on improving the accuracy and confidence of predictions, enhancing scalability for large datasets, and extending its capabilities to ranking problems. These algorithms offer a valuable alternative to "black box" models like XGBoost and MLPs by providing human-interpretable rules, thereby increasing transparency and trust in machine learning applications, especially in domains requiring accountability such as education and healthcare.

Papers

October 27, 2024

FoldMark: Protecting Protein Generative Models with Watermarking
Zaixi Zhang, Ruofan Jin, Kaidi Fu, Le Cong, Marinka Zitnik, Mengdi Wang
Agnostic Watermarking Two Stage Protein Structure Fold Rm

August 14, 2024

CON-FOLD -- Explainable Machine Learning with Confidence
Lachlan McGinness, Peter Baumgartner
High Confidence Explainable Machine Learning Interpretable Machine Based Pruning Classification Rule Fold Rm

August 16, 2022

FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability
Huaduo Wang, Gopal Gupta
High Explainability Rule Based Interpretable Machine Fold Rm

June 15, 2022

FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank
Huaduo Wang, Gopal Gupta
LeArning Abstract Fold Rm

February 14, 2022

FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data
Huaduo Wang, Farhad Shakerin, Gopal Gupta
Potential Scalability Model Interpretability Explainable Model Inductive Learning Mixed Data Multi Category Fold Rm

Fold Rm

Papers

FoldMark: Protecting Protein Generative Models with Watermarking

CON-FOLD -- Explainable Machine Learning with Confidence

FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability

FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank

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