{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ensemble-representation-learning-an-analysis","title":"Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods","arxiv_id":"1703.06934","date":"2017-03-20","proceeding":null,"authors":["William La Cava","Jason H. Moore"],"abstract":"Recently we proposed a general, ensemble-based feature engineering wrapper\n(FEW) that was paired with a number of machine learning methods to solve\nregression problems. Here, we adapt FEW for supervised classification and\nperform a thorough analysis of fitness and survival methods within this\nframework. Our tests demonstrate that two fitness metrics, one introduced as an\nadaptation of the silhouette score, outperform the more commonly used Fisher\ncriterion. We analyze survival methods and demonstrate that $\\epsilon$-lexicase\nsurvival works best across our test problems, followed by random survival which\noutperforms both tournament and deterministic crowding. We conduct a benchmark\ncomparison to several classification methods using a large set of problems and\nshow that FEW can improve the best classifier performance in several cases. We\nshow that FEW generates consistent, meaningful features for a biomedical\nproblem with different ML pairings.","url_abs":"http://arxiv.org/abs/1703.06934v3","url_pdf":"http://arxiv.org/pdf/1703.06934v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ensemble-representation-learning-an-analysis","repo_url":"https://github.com/lacava/few","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}