{"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/a-new-fuzzy-stacked-generalization-technique","title":"A New Fuzzy Stacked Generalization Technique and Analysis of its Performance","arxiv_id":"1204.0171","date":"2012-04-01","proceeding":null,"authors":["Mete Ozay","Fatos T. Yarman Vural"],"abstract":"In this study, a new Stacked Generalization technique called Fuzzy Stacked\nGeneralization (FSG) is proposed to minimize the difference between N -sample\nand large-sample classification error of the Nearest Neighbor classifier. The\nproposed FSG employs a new hierarchical distance learning strategy to minimize\nthe error difference. For this purpose, we first construct an ensemble of\nbase-layer fuzzy k- Nearest Neighbor (k-NN) classifiers, each of which receives\na different feature set extracted from the same sample set. The fuzzy\nmembership values computed at the decision space of each fuzzy k-NN classifier\nare concatenated to form the feature vectors of a fusion space. Finally, the\nfeature vectors are fed to a meta-layer classifier to learn the degree of\naccuracy of the decisions of the base-layer classifiers for meta-layer\nclassification. Rather than the power of the individual base layer-classifiers,\ndiversity and cooperation of the classifiers become an important issue to\nimprove the overall performance of the proposed FSG. A weak base-layer\nclassifier may boost the overall performance more than a strong classifier, if\nit is capable of recognizing the samples, which are not recognized by the rest\nof the classifiers, in its own feature space. The experiments explore the type\nof the collaboration among the individual classifiers required for an improved\nperformance of the suggested architecture. Experiments on multiple feature\nreal-world datasets show that the proposed FSG performs better than the state\nof the art ensemble learning algorithms such as Adaboost, Random Subspace and\nRotation Forest. On the other hand, compatible performances are observed in the\nexperiments on single feature multi-attribute datasets.","url_abs":"http://arxiv.org/abs/1204.0171v5","url_pdf":"http://arxiv.org/pdf/1204.0171v5.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":"a-new-fuzzy-stacked-generalization-technique","repo_url":"https://github.com/meteozay/fsg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"k-nn","method_name":"k-NN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}