{"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-of-heterogeneous-flexible-neural","title":"Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming","arxiv_id":"1705.05592","date":"2017-05-16","proceeding":null,"authors":["Varun Kumar Ojha","Ajith Abraham","Václav Snášel"],"abstract":"Machine learning algorithms are inherently multiobjective in nature, where\napproximation error minimization and model's complexity simplification are two\nconflicting objectives. We proposed a multiobjective genetic programming (MOGP)\nfor creating a heterogeneous flexible neural tree (HFNT), tree-like flexible\nfeedforward neural network model. The functional heterogeneity in neural tree\nnodes was introduced to capture a better insight of data during learning\nbecause each input in a dataset possess different features. MOGP guided an\ninitial HFNT population towards Pareto-optimal solutions, where the final\npopulation was used for making an ensemble system. A diversity index measure\nalong with approximation error and complexity was introduced to maintain\ndiversity among the candidates in the population. Hence, the ensemble was\ncreated by using accurate, structurally simple, and diverse candidates from\nMOGP final population. Differential evolution algorithm was applied to\nfine-tune the underlying parameters of the selected candidates. A comprehensive\ntest over classification, regression, and time-series datasets proved the\nefficiency of the proposed algorithm over other available prediction methods.\nMoreover, the heterogeneous creation of HFNT proved to be efficient in making\nensemble system from the final population.","url_abs":"http://arxiv.org/abs/1705.05592v1","url_pdf":"http://arxiv.org/pdf/1705.05592v1.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-of-heterogeneous-flexible-neural","repo_url":"https://github.com/vojha-code/Neural-Tree-Software","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-regression","task_name":"Time Series Regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}