{"id":50306882,"url":"https://github.com/cissagatto/hpml-chains","last_synced_at":"2026-05-28T17:01:55.074Z","repository":{"id":204492310,"uuid":"582682349","full_name":"cissagatto/HPML-Chains","owner":"cissagatto","description":"This code is a part of my doctoral research at PPG-CC/DC/UFSCar in colaboration with Ku Leuven in Belgium.","archived":false,"fork":false,"pushed_at":"2024-09-04T15:37:23.000Z","size":2339,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-09-05T21:49:07.512Z","etag":null,"topics":["classifier-chains","ensemble","java","label-correlations","label-space-partitioning","machine-learning","multi-label-classification","multi-label-clustering","multi-label-partition","multi-label-partitioning","r-language"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cissagatto.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-12-27T15:10:52.000Z","updated_at":"2024-09-04T15:37:26.000Z","dependencies_parsed_at":null,"dependency_job_id":"2566199c-0107-4318-82a7-22493220db28","html_url":"https://github.com/cissagatto/HPML-Chains","commit_stats":{"total_commits":20,"total_committers":1,"mean_commits":20.0,"dds":0.0,"last_synced_commit":"80c112bdee6e774b51738acba1abc9d4f783ae0e"},"previous_names":["cissagatto/hpml-chains"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/cissagatto/HPML-Chains","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cissagatto%2FHPML-Chains","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cissagatto%2FHPML-Chains/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cissagatto%2FHPML-Chains/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cissagatto%2FHPML-Chains/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cissagatto","download_url":"https://codeload.github.com/cissagatto/HPML-Chains/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cissagatto%2FHPML-Chains/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33617718,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-05-28T02:00:06.440Z","response_time":99,"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":["classifier-chains","ensemble","java","label-correlations","label-space-partitioning","machine-learning","multi-label-classification","multi-label-clustering","multi-label-partition","multi-label-partitioning","r-language"],"created_at":"2026-05-28T17:01:53.632Z","updated_at":"2026-05-28T17:01:55.068Z","avatar_url":"https://github.com/cissagatto.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# HPML Experiment 4: Chains\nThis is the 4º experiment for HPML which is based on Classifier Chains.\n\n## Enviroments to run experiments\nClick here to download \n\n### Conda\nYou can run this experiment in Conda Environment. The name is \"AmbienteTeste\". To be able to use this env you must first install conda in your computer or server and then install the environment using the following command: *conda env create --file AmbienteTeste.yaml*\n\n### Singularity\nYou can also run this experiment in a singularity container. Download the recipes and follow this tutorial (in portuguese): https://prensa.li/@cissa.gatto/tutorial-como-criar-um-container-singularity-para-executar-scripts-r-com-java-e-rclone\n\n## Code\n\n### Step 1\n\nPre-processing. [10-Fold Cross Validation](https://github.com/cissagatto/CrossValidationMultiLabel)\n\n### Step 2 and 3\n\nModeling the correlations between the labels and choosing the best dendrogram to generate the hybrid partitions. [Code](https://github.com/cissagatto/jaccard)\n\n### Step 4, 5 and 6\n\nBuilding and validating all generated hybrid partitions with the silhouette coefficient. The hybrid partition with the highiest silhouet coefficient is chosen to be tested. [Code](https://github.com/cissagatto/Best-Partition-Silhouette)\n\n\n### Step 7\n\nBuilding and testing the best chosen hybrid partition.\n\n[HPML.D](https://github.com/cissagatto/HPML.D.padrao)\n\n[HPML.D.CI](https://github.com/cissagatto/HPML.D.CI)\n\n[HPML.D.CE](https://github.com/cissagatto/HPML.D.CE)\n\n[HPML.D.CEI](https://github.com/cissagatto/HPML.D.CEI)\n\n## Global Partitions\n[Code](https://github.com/cissagatto/Global-Partitions)\n\n## Local Partitions\n[Code](https://github.com/cissagatto/Local-Partitions)\n\n\n## Download Results\n- [Original Multi-Label Datasets](https://cometa.ujaen.es/datasets/)\n- [10-Fold Cross Validation Multi-Label Datasets](https://www.4shared.com/s/dYpGZWzjQ)\n- [Jaccard Best Dendrograms](https://www.4shared.com/folder/wVsBXIT5/1-Jaccard-Best-Dendrograms.html)\n- [Best Partitions from Silhouette Coefficient](https://www.4shared.com/folder/ucwVLJIg/2-Best-Partitions-Silhouette.html)\n- Test\n\n\n## Acknowledgment\n- This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.\n- This study was financed in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brasil (CNPQ) - Process number 200371/2022-3.\n- The authors also thank the Brazilian research agencies FAPESP financial support.\n\n# Contact\nelainececiliagatto@gmail.com\n\n## Links\n\n| [Site](https://sites.google.com/view/professor-cissa-gatto) | [Post-Graduate Program in Computer Science](http://ppgcc.dc.ufscar.br/pt-br) | [Computer Department](https://site.dc.ufscar.br/) |  [Biomal](http://www.biomal.ufscar.br/) | [CNPQ](https://www.gov.br/cnpq/pt-br) | [Ku Leuven](https://kulak.kuleuven.be/) | [Embarcados](https://www.embarcados.com.br/author/cissa/) | [Read Prensa](https://prensa.li/@cissa.gatto/) | [Linkedin Company](https://www.linkedin.com/company/27241216) | [Linkedin Profile](https://www.linkedin.com/in/elainececiliagatto/) | [Instagram](https://www.instagram.com/cissagatto) | [Facebook](https://www.facebook.com/cissagatto) | [Twitter](https://twitter.com/cissagatto) | [Twitch](https://www.twitch.tv/cissagatto) | [Youtube](https://www.youtube.com/CissaGatto) |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcissagatto%2Fhpml-chains","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcissagatto%2Fhpml-chains","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcissagatto%2Fhpml-chains/lists"}