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ferryjul / fairCORELS

Algorithm for learning fair rule lists
GNU General Public License v3.0
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how different ideas and performance from other rule based machine learning packages #2

Closed Sandy4321 closed 4 years ago

Sandy4321 commented 4 years ago

how different ideas and performance from other rule based machine learning packages arulesViz arulesExplain arules arc arulesCBA https://journal.r-project.org/archive/2019/RJ-2019-048/RJ-2019-048.pdf rCBA EasyMiner LISp-Miner desktop-based implementation of the GUHA
rsubgroup
Vikamine https://github.com/fingoldin/pycorels
https://github.com/lukassykora/actionrules https://github.com/jirifilip/pyIDS https://github.com/csinva/interpretability-implementations-demos/tree/master/imodels/bayesian_rule_lists

sorry for too complicated question ....

Sandy4321 commented 4 years ago

also it would be interesting to run your code vs pyARC all algorithms M1 M1Algorithm,

M2 M2Algorithm http://ceur-ws.org/Vol-2204/paper6.pdf and QCBA https://github.com/jirifilip/pyARC/blob/master/notebooks/qcba_extension/QCBA_benchmark.ipynb

by use data from https://github.com/fingoldin/pycorels/tree/master/examples/data

ferryjul commented 4 years ago

Thank you for your interest in fairCORELS !

Concerning the comparison with fairCORELS, the different models cited in your question do not deal with bias mitigation and thus should not directly be compared with fairCORELS.

Sandy4321 commented 4 years ago

Looks interesting May you share some links to description How this bias mitigation happen? And hopefully you do have code examples

gbip commented 4 years ago

How this bias mitigation happen?

You should take a look at this paper on arxiv which is the publication associated with this project. Please correct me @ferryjul if I am wrong.