{"id":28532341,"url":"https://github.com/predict-idlab/genesim","last_synced_at":"2026-01-29T12:40:00.793Z","repository":{"id":69686085,"uuid":"71539505","full_name":"predict-idlab/GENESIM","owner":"predict-idlab","description":"[DEPRECATED]  An innovative technique that constructs an ensemble of decision trees and converts this ensemble into a single, interpretable decision tree with an enhanced predictive performance","archived":false,"fork":false,"pushed_at":"2021-01-12T07:15:32.000Z","size":4062,"stargazers_count":80,"open_issues_count":1,"forks_count":14,"subscribers_count":15,"default_branch":"master","last_synced_at":"2025-07-01T12:26:49.180Z","etag":null,"topics":["data-mining","decision-trees","ensemble"],"latest_commit_sha":null,"homepage":"","language":"Scilab","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/predict-idlab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2016-10-21T07:10:40.000Z","updated_at":"2025-05-07T06:14:37.000Z","dependencies_parsed_at":null,"dependency_job_id":"3d495dd7-8c45-414d-974f-6fdda9626408","html_url":"https://github.com/predict-idlab/GENESIM","commit_stats":null,"previous_names":["predict-idlab/genesim","ibcnservices/genesim"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/predict-idlab/GENESIM","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/predict-idlab%2FGENESIM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/predict-idlab%2FGENESIM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/predict-idlab%2FGENESIM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/predict-idlab%2FGENESIM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/predict-idlab","download_url":"https://codeload.github.com/predict-idlab/GENESIM/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/predict-idlab%2FGENESIM/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28877876,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-29T10:31:27.438Z","status":"ssl_error","status_checked_at":"2026-01-29T10:31:01.017Z","response_time":59,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-mining","decision-trees","ensemble"],"created_at":"2025-06-09T15:38:12.432Z","updated_at":"2026-01-29T12:40:00.788Z","avatar_url":"https://github.com/predict-idlab.png","language":"Scilab","funding_links":[],"categories":[],"sub_categories":[],"readme":"# GENESIM: GENetic Extraction of a Single, Interpretable Model\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"evolving_tree.gif\"\u003e\n\u003c/p\u003e\n\nThis repository contains an innovative algorithm that constructs an ensemble using well-known decision tree induction algorithms such as CART, C4.5, QUEST and GUIDE combined with bagging and boosting. Then, this ensemble is converted to a single, interpretable decision tree in a genetic fashion. For a certain number of iterations, random pairs of decision trees are merged together by first converting them to sets of k-dimensional hyperplanes and then calculating the intersection of these two sets (a classic problem from computational geometry). Moreover, in each iteration, an individual is mutated with a certain probabibility. After these iterations, the accuracy on a validation set is measured for each of the decision trees in the population and the one with the highest accuracy (and lowest number of nodes in case of a tie) is returned. Example.py has run code for all implemented algorithms and returns their average predictive performance, computational complexity and model complexity on a number of dataset\n\n## Dependencies\n\nAn install.sh script is provided that will install all required dependencies\n\n## Documentation\n\nA nicely looking documentation page is available in the doc/ directory. Download the complete directory and open index.html\n\n## Decision Tree Induction Algorithm Wrappers\n\nA wrapper is written around [Orange C4.5](http://docs.orange.biolab.si/2/reference/rst/Orange.classification.tree.html#Orange.classification.tree.C45Learner), [sklearn CART](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html), [GUIDE](https://www.stat.wisc.edu/~loh/guide.html) and [QUEST](https://www.stat.wisc.edu/~loh/quest.html). The returned object is a Decision Tree, which can be found in `decisiontree.py`. Moreover, different methods are available on this decision tree: classify new, unknown samples; visualise the tree; export it to string, JSON and DOT; etc.\n\n## Ensemble Technique Wrappers\n\nA wrapper is written around the well-known state-of-the-art ensemble techniques [XGBoost](http://xgboost.readthedocs.io/en/latest/python/python_intro.html) and [Random Forests](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)\n\n## Similar techniques\n\nA wrapper written around the R package [inTrees](https://arxiv.org/abs/1408.5456) and an implementation of [ISM](https://lirias.kuleuven.be/handle/123456789/146229) can be found in the constructors package.\n\n## New dataset\n\nA new dataset can easily be plugged in into the benchmark. For this, a `load_dataset()` function must be written in `load_datasets.py`\n\n## Contact\n\nYou can contact me at givdwiel.vandewiele at ugent.be for any questions, proposals or if you wish to contribute.\n\n## Referring\n\nPlease refer to my work when you use it. A reference to this github or to the following (yet unpublished) paper:\n\n`\n@article{vandewiele2016genesim,\n  title={GENESIM: genetic extraction of a single, interpretable model},\n  author={Vandewiele, Gilles and Janssens, Olivier and Ongenae, Femke and De Turck, Filip and Van Hoecke, Sofie},\n  journal={arXiv preprint arXiv:1611.05722},\n  year={2016}\n}\n`\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpredict-idlab%2Fgenesim","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpredict-idlab%2Fgenesim","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpredict-idlab%2Fgenesim/lists"}