5 citations · 8 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
Induction of Non-monotonic Logic Programs To Explain Statistical Learning Models
Farhad Shakerin
We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models. We reduce the problem of search for best clauses to instances of t…
Induction of Non-Monotonic Rules From Statistical Learning Models Using High-Utility Itemset Mining
Farhad Shakerin, Gopal Gupta
We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models. We reduce the problem of search for best clauses to instances of t…
Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME
Farhad Shakerin, Gopal Gupta
We present a heuristic based algorithm to induce \textit{nonmonotonic} logic programs that will explain the behavior of XGBoost trained classifiers. We use the technique based on t…