{"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/multiobjective-programming-for-type-2","title":"Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees","arxiv_id":"1705.05769","date":"2017-05-16","proceeding":null,"authors":["Varun Kumar Ojha","Vaclav Snasel","Ajith Abraham"],"abstract":"This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An\nHFIT produces an optimum treelike structure, i.e., a natural hierarchical\nstructure that accommodates simplicity by combining several low-dimensional\nfuzzy inference systems (FISs). Such a natural hierarchical structure provides\na high degree of approximation accuracy. The construction of HFIT takes place\nin two phases. Firstly, a nondominated sorting based multiobjective genetic\nprogramming (MOGP) is applied to obtain a simple tree structure (a low\ncomplexity model) with a high accuracy. Secondly, the differential evolution\nalgorithm is applied to optimize the obtained tree's parameters. In the derived\ntree, each node acquires a different input's combination, where the\nevolutionary process governs the input's combination. Hence, HFIT nodes are\nheterogeneous in nature, which leads to a high diversity among the rules\ngenerated by the HFIT. Additionally, the HFIT provides an automatic feature\nselection because it uses MOGP for the tree's structural optimization that\naccepts inputs only relevant to the knowledge contained in data. The HFIT was\nstudied in the context of both type-1 and type-2 FISs, and its performance was\nevaluated through six application problems. Moreover, the proposed\nmultiobjective HFIT was compared both theoretically and empirically with\nrecently proposed FISs methods from the literature, such as McIT2FIS,\nTSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the\nobtained results, it was found that the HFIT provided less complex and highly\naccurate models compared to the models produced by the most of other methods.\nHence, the proposed HFIT is an efficient and competitive alternative to the\nother FISs for function approximation and feature selection.","url_abs":"http://arxiv.org/abs/1705.05769v1","url_pdf":"http://arxiv.org/pdf/1705.05769v1.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":"multiobjective-programming-for-type-2","repo_url":"https://github.com/vojha-code/Hierarchical-Fuzzy-Tree","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":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-regression","task_name":"Time Series Regression"},{"task_slug":"type","task_name":"Vocal Bursts Type Prediction"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}