rule-learning

There are 0 repository under rule-learning topic.

imodels
csinva / imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
interpretability machine-learning data-science artificial-intelligence ml ai statistics scikit-learn python optimal-classification-tree rulefit imodels rule-learning supervised-learning explainable-ml explainable-ai rules bayesian-rule-list
Language:Jupyter Notebook 1510
adaa-polsl / RuleKit
Comprehensive suite for rule-based learning
rule-induction rule-learning knowledge-discovery
Language:Java 169
linkedin / TE2Rules
Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.
explainability explainable-ai interpretability interpretable-ai machine-learning random-forest rule-learning tree-ensembles xgboost
Language:Python 60
rz-zhang / PRBoost
The codes for our ACL'22 paper: PRBOOST: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning.
weakly-supervised-learning weak-supervision rule-learning rule-discovery online-learning iterative-learning interactive-learning prompt boosting adaptive-learning nlp
35
xcsf-dev / xcsf
XCSF learning classifier system: rule-based online evolutionary machine learning
learning-classifier-systems xcsf xcs evolutionary-algorithms reinforcement-learning supervised-learning stochastic-gradient-descent neural-networks neuroevolution unsupervised-learning least-squares genetic-programming machine-learning divide-and-conquer rule-based online-learning artificial-intelligence rule-learning
Language:C 34
Boomer
mrapp-ke / Boomer
A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-label Classification Rules
gradient-boosting rule-learning multilabel-classification machine-learning scikit-learn
21
nju-websoft / RGRec
Rule-Guided Graph Neural Networks for Recommender Systems, ISWC 2020
recommender-system rule-learning graph-neural-network knowledge-graph
Language:Python 17
ParrotPrediction / pyalcs
Implementation of Anticipatory Learning Classifiers System (ALCS) in Python
python machine-learning reinforcement-learning learning-classifier-systems anticipatory-classifier-systems rule-learning genetic-algorithms
Language:Python 10
lab-v2 / PyEDCR
PyEDCR is a metacognitive neuro-symbolic method for learning error detection and correction rules in deployed ML models using combinatorial sub-modular set optimization
artificial-intelligence data-science deep-learning error-detection-correction hierarchical-classification logic machine-learning metacognition neuro-symbolic-ai neuro-symbolic-learning meta rule-learning submodular-optimization computer-vision dino-v2 vision-transformer military-vehicles logic-programming multi-label-classification noise-tolerance
Language:Python 6
vqphuynh / LORD
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)
machine-learning rule-learning data-mining
Language:Java 5
groshanlal / NN2Rules
Explain fully connected ReLU neural networks using rules
explainability explainable-ai fully-connected-deep-neural-network interpretability interpretable-ai machine-learning neural-networks relu-activation rule-learning
Language:Python 4
MLRL-Boomer
mrapp-ke / MLRL-Boomer
A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules
gradient-boosting multilabel-classification rule-learning machine-learning scikit-learn multi-target-regression multioutput-regressor
Language:C++ 3
martinsvat / Pruning-Hypotheses
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
first-order-logic pruning search-algorithm relational-logic rule-learning
Language:Java 1
mrapp-ke / Boomer-Doc
Documentation of the BOOMER machine learning algorithm.
machine-learning scikit-learn gradient-boosting multi-label-classification rule-learning
1
martinsvat / STRiKE
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
inference knowledge-base-completion knowledge-graph-completion machine-learning relational-learning rule-learning
Language:Java 0
mrapp-ke / SyndromeLearner
A rule learning algorithm for the deduction of syndrome definitions from time series data.
machine-learning rule-learning time-series-analysis
Language:C++ 0
vqphuynh / P3Ctree
Implementation of P3C-tree (Partial Pre-Post Code Tree) published in KDD2025. P3C-tree is significantly saves memory compared with PPC-tree, by factors of some to some tens and runs faster while generating the same N-lists for a given input data.
algorithms big-data-mining data-mining data-structures efficiency rule-learning
Language:Java

rule-learning

csinva / imodels

adaa-polsl / RuleKit

linkedin / TE2Rules

rz-zhang / PRBoost

xcsf-dev / xcsf

mrapp-ke / Boomer

nju-websoft / RGRec

ParrotPrediction / pyalcs

lab-v2 / PyEDCR

vqphuynh / LORD

groshanlal / NN2Rules

mrapp-ke / MLRL-Boomer

martinsvat / Pruning-Hypotheses

mrapp-ke / Boomer-Doc

martinsvat / STRiKE

mrapp-ke / SyndromeLearner

vqphuynh / P3Ctree