7 citations · 35 across the 14 of their papers we have counts for
7 papers · 1 filter
Comparative Analysis of FOLD-SE vs. FOLD-R++ in Binary Classification and XGBoost in Multi-Category Classification
Akshay Murthy, Shawn Sebastian, Manil Shangle +3
Recently, the demand for Machine Learning (ML) models that can balance accuracy, efficiency, and interpreability has grown significantly. Traditionally, there has been a tradeoff b…
Superfast Selection for Decision Tree Algorithms
Huaduo Wang, Gopal Gupta
We present a novel and systematic method, called Superfast Selection, for selecting the "optimal split" for decision tree and feature selection algorithms over tabular data. The me…
NeSyFOLD: Neurosymbolic Framework for Interpretable Image Classification
Parth Padalkar, Huaduo Wang, Gopal Gupta
Deep learning models such as CNNs have surpassed human performance in computer vision tasks such as image classification. However, despite their sophistication, these models lack i…
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability
Huaduo Wang, Gopal Gupta
We present FOLD-SE, an efficient, explainable machine learning algorithm for classification tasks given tabular data containing numerical and categorical values. FOLD-SE generates…
FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank
Huaduo Wang, Gopal Gupta
FOLD-R++ is a new inductive learning algorithm for binary classification tasks. It generates an (explainable) normal logic program for mixed type (numerical and categorical) data.…
FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data
Huaduo Wang, Farhad Shakerin, Gopal Gupta
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 (AS…
FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data
Huaduo Wang, Gopal Gupta
FOLD-R is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP…