{"id":17357683,"url":"https://github.com/jd557/weka-emiodc","last_synced_at":"2025-09-05T10:33:05.033Z","repository":{"id":41160113,"uuid":"52167533","full_name":"JD557/weka-emiodc","owner":"JD557","description":"Weka Package containing multiple ordinal data classifiers","archived":false,"fork":false,"pushed_at":"2016-03-21T19:57:11.000Z","size":60,"stargazers_count":6,"open_issues_count":0,"forks_count":4,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-08-31T23:58:41.656Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Java","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/JD557.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-02-20T18:06:32.000Z","updated_at":"2024-12-31T15:42:13.000Z","dependencies_parsed_at":"2022-09-01T15:24:30.030Z","dependency_job_id":null,"html_url":"https://github.com/JD557/weka-emiodc","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/JD557/weka-emiodc","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JD557%2Fweka-emiodc","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JD557%2Fweka-emiodc/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JD557%2Fweka-emiodc/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JD557%2Fweka-emiodc/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/JD557","download_url":"https://codeload.github.com/JD557/weka-emiodc/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JD557%2Fweka-emiodc/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273746631,"owners_count":25160646,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-05T02:00:09.113Z","response_time":402,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":[],"created_at":"2024-10-15T19:01:31.504Z","updated_at":"2025-09-05T10:33:00.004Z","avatar_url":"https://github.com/JD557.png","language":"Java","funding_links":[],"categories":[],"sub_categories":[],"readme":"Ensemble Methods in Ordinal Classification\n==========================================\n\nThis repository contains the implementation of the various algorithms presented\nin [Ensemble Methods in Ordinal Data Classification](https://repositorio-aberto.up.pt/handle/10216/73795?locale=en)\nfor Weka 3.7.\n\nIncluded Algorithms\n-------------------\n\n* oAdaBoost: [oAdaBoost: An AdaBoost variant for Ordinal Data Classification](http://joaocosta.eu/Portfolio/docs/pubs/oAdaboost2015.pdf)\n* AdaBoost.OR: [Combining ordinal preferences by boosting](https://www.csie.ntu.edu.tw/~htlin/paper/doc/wspl09adaboostor.pdf)\n* AdaBoost.M1w: [How to make AdaBoost.M1 work for weak base classifiers by changing only one line of the code](http://www.en-trust.at/eibl/wp-content/uploads/sites/3/2013/08/Eibl02_ECML_AdaBoostM1W.pdf)\n* oDT: [Ensemble Methods in Ordinal Data Classification](https://repositorio-aberto.up.pt/bitstream/10216/73795/2/99372.pdf)\n* Ordinal Random Forests: [Ensemble Methods in Ordinal Data Classification](https://repositorio-aberto.up.pt/bitstream/10216/73795/2/99372.pdf)\n\nInstalation\n-----------\n\n### Download (Recommended)\n\n1. Download the pre-compiled package from https://github.com/JD557/weka-emiodc/releases\n2. Open Weka 3.7 and choose `Tools \u003e Package Manager`\n3. Click on `File/URL` (under `Unofficial`) and choose the downloaded .zip\n4. Restart Weka\n5. You should now an `OrdinalEnsembleMethods` package\n6. The new classifiers should now be available\n\n### Compile from source\n\n1. Run `sbt package`\n2. Compress the compiled jar (`target/scala-2.10/emiodc_2.10-1.0.jar`) alongside the `Description.props` in a zip\n2. Open Weka 3.7 and choose `Tools \u003e Package Manager`\n3. Click on `File/URL` (under `Unofficial`) and choose the generated .zip\n4. Restart Weka\n5. You should now an `OrdinalEnsembleMethods` package\n6. The new classifiers should now be available\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjd557%2Fweka-emiodc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjd557%2Fweka-emiodc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjd557%2Fweka-emiodc/lists"}