{"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/scalable-twin-neural-networks-for","title":"Scalable Twin Neural Networks for Classification of Unbalanced Data","arxiv_id":"1705.00347","date":"2017-04-30","proceeding":null,"authors":["Jayadeva","Himanshu Pant","Sumit Soman","Mayank Sharma"],"abstract":"Twin Support Vector Machines (TWSVMs) have emerged an efficient alternative\nto Support Vector Machines (SVM) for learning from imbalanced datasets. The\nTWSVM learns two non-parallel classifying hyperplanes by solving a couple of\nsmaller sized problems. However, it is unsuitable for large datasets, as it\ninvolves matrix operations. In this paper, we discuss a Twin Neural Network\n(Twin NN) architecture for learning from large unbalanced datasets. The Twin NN\nalso learns an optimal feature map, allowing for better discrimination between\nclasses. We also present an extension of this network architecture for\nmulticlass datasets. Results presented in the paper demonstrate that the Twin\nNN generalizes well and scales well on large unbalanced datasets.","url_abs":"http://arxiv.org/abs/1705.00347v2","url_pdf":"http://arxiv.org/pdf/1705.00347v2.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":"scalable-twin-neural-networks-for","repo_url":"https://github.com/panthimanshu/twinNeuralNets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}