{"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/cifar-10-knn-based-ensemble-of-classifiers","title":"CIFAR-10: KNN-based Ensemble of Classifiers","arxiv_id":"1611.04905","date":"2016-11-15","proceeding":null,"authors":["Yehya Abouelnaga","Ola S. Ali","Hager Rady","Mohamed Moustafa"],"abstract":"In this paper, we study the performance of different classifiers on the\nCIFAR-10 dataset, and build an ensemble of classifiers to reach a better\nperformance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and\nConvolutional Neural Network (CNN), on some classes, are mutually exclusive,\nthus yield in higher accuracy when combined. We reduce KNN overfitting using\nPrincipal Component Analysis (PCA), and ensemble it with a CNN to increase its\naccuracy. Our approach improves our best CNN model from 93.33% to 94.03%.","url_abs":"http://arxiv.org/abs/1611.04905v1","url_pdf":"http://arxiv.org/pdf/1611.04905v1.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":"cifar-10-knn-based-ensemble-of-classifiers","repo_url":"https://github.com/tushargoyal9990/Image-Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}