Rule-Guided Graph Neural Networks for Recommender Systems, ISWC 2020
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Updated
Sep 14, 2020 - Python
Rule-Guided Graph Neural Networks for Recommender Systems, ISWC 2020
A rule learning algorithm for the deduction of syndrome definitions from time series data.
The codes for our ACL'22 paper: PRBOOST: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning.
Explain fully connected ReLU neural networks using rules
A Java implementation for LORD, a rule learning algorithm proposed in the article "Efficient learning of large sets of locally optimal classification rules" with the approach of searching for a locally optimal rule for each training example. Machine Learning, volume 112, pages 571–610 (2023)
Implementation of Anticipatory Learning Classifiers System (ALCS) in Python
Implementation of pruning hypothesis space using domain theories -- M. Svatoš, G. Šourek, F. Zeležný, S. Schockaert, and O. Kuželka: Pruning Hypothesis Spaces Using Learned Domain Theories, ILP'17
Implementation of a learning and fragment-based rule inference engine -- M. Svatoš, S. Schockaert, J. Davis, and O. Kuželka: STRiKE: Rule-driven relational learning using stratified k-entailment, ECAI'20
Documentation of the BOOMER machine learning algorithm.
A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-label Classification Rules
Comprehensive suite for rule-based learning
Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-label Classification Rules
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