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Topic: "rule-learning"

csinva/imodels

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Language: Jupyter Notebook - Size: 162 MB - Last synced at: 1 day ago - Pushed at: 2 months ago - Stars: 1,454 - Forks: 124

adaa-polsl/RuleKit

Comprehensive suite for rule-based learning

Language: Java - Size: 9.4 MB - Last synced at: 10 days ago - Pushed at: 10 days ago - Stars: 153 - Forks: 10

linkedin/TE2Rules

Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.

Language: Python - Size: 10.9 MB - Last synced at: 12 days ago - Pushed at: about 1 year ago - Stars: 55 - Forks: 6

xcsf-dev/xcsf

XCSF learning classifier system: rule-based online evolutionary machine learning

Language: C - Size: 49 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 32 - Forks: 13

rz-zhang/PRBoost

The codes for our ACL'22 paper: PRBOOST: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning.

Size: 4.88 KB - Last synced at: almost 2 years ago - Pushed at: about 3 years ago - Stars: 31 - Forks: 0

mrapp-ke/Boomer 📦

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-label Classification Rules

Size: 200 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 19 - Forks: 3

nju-websoft/RGRec

Rule-Guided Graph Neural Networks for Recommender Systems, ISWC 2020

Language: Python - Size: 60.3 MB - Last synced at: 12 months ago - Pushed at: over 4 years ago - Stars: 16 - Forks: 5

ParrotPrediction/pyalcs

Implementation of Anticipatory Learning Classifiers System (ALCS) in Python

Language: Python - Size: 13.1 MB - Last synced at: 7 days ago - Pushed at: almost 2 years ago - Stars: 10 - Forks: 16

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

Language: Python - Size: 16.2 GB - Last synced at: 23 days ago - Pushed at: 3 months ago - Stars: 4 - Forks: 1

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)

Language: Java - Size: 34.8 MB - Last synced at: almost 2 years ago - Pushed at: about 2 years ago - Stars: 2 - Forks: 0

groshanlal/NN2Rules

Explain fully connected ReLU neural networks using rules

Language: Python - Size: 66.4 KB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 2 - Forks: 0

mrapp-ke/MLRL-Boomer

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

Language: C++ - Size: 311 MB - Last synced at: 3 days ago - Pushed at: 3 days ago - Stars: 1 - Forks: 0

mrapp-ke/Boomer-Doc 📦

Documentation of the BOOMER machine learning algorithm.

Size: 16.4 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

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

Language: Java - Size: 22.7 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

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

Language: Java - Size: 310 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

mrapp-ke/SyndromeLearner

A rule learning algorithm for the deduction of syndrome definitions from time series data.

Language: C++ - Size: 164 KB - Last synced at: about 1 month ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 1

Related Topics
machine-learning 12 scikit-learn 4 artificial-intelligence 3 interpretability 3 explainable-ai 3 gradient-boosting 3 multilabel-classification 2 neural-networks 2 interpretable-ai 2 supervised-learning 2 explainability 2 multi-label-classification 2 data-science 2 online-learning 2 learning-classifier-systems 2 python 2 reinforcement-learning 2 knowledge-discovery 1 rule-induction 1 divide-and-conquer 1 evolutionary-algorithms 1 multioutput-regressor 1 statistics 1 multi-target-regression 1 xgboost 1 tree-ensembles 1 random-forest 1 vision-transformer 1 submodular-optimization 1 noise-tolerance 1 neuro-symbolic-learning 1 neuro-symbolic-ai 1 military-vehicles 1 rules 1 rulefit 1 optimal-classification-tree 1 ml 1 imodels 1 explainable-ml 1 bayesian-rule-list 1 ai 1 time-series-analysis 1 xcsf 1 xcs 1 unsupervised-learning 1 stochastic-gradient-descent 1 rule-based 1 neuroevolution 1 least-squares 1 genetic-programming 1 genetic-algorithms 1 anticipatory-classifier-systems 1 recommender-system 1 knowledge-graph 1 graph-neural-network 1 data-mining 1 relu-activation 1 fully-connected-deep-neural-network 1 weakly-supervised-learning 1 weak-supervision 1 rule-discovery 1 prompt 1 nlp 1 iterative-learning 1 interactive-learning 1 boosting 1 adaptive-learning 1 metacognition 1 meta 1 logic-programming 1 logic 1 hierarchical-classification 1 error-detection-correction 1 dino-v2 1 deep-learning 1 computer-vision 1 relational-learning 1 knowledge-graph-completion 1 knowledge-base-completion 1 inference 1 search-algorithm 1 relational-logic 1 pruning 1 first-order-logic 1