{"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/optimal-margin-evolutionary-classifier","title":"Optimal-margin evolutionary classifier","arxiv_id":"1804.09891","date":"2018-04-26","proceeding":null,"authors":["Mohammad Reza Bonyadi","David C. Reutens"],"abstract":"We introduce a novel approach for discriminative classification using\nevolutionary algorithms. We first propose an algorithm to optimize the total\nloss value using a modified 0-1 loss function in a one-dimensional space for\nclassification. We then extend this algorithm for multi-dimensional\nclassification using an evolutionary algorithm. The proposed evolutionary\nalgorithm aims to find a hyperplane which best classifies instances while\nminimizes the classification risk. We test particle swarm optimization,\nevolutionary strategy, and covariance matrix adaptation evolutionary strategy\nfor optimization purpose. Finally, we compare our results with well-established\nand state-of-the-art classification algorithms, for both binary and multi-class\nclassification, on 19 benchmark classification problems, with and without noise\nand outliers. Results show that the performance of the proposed algorithm is\nsignificantly (t-test) better than all other methods in almost all problems\ntested. We also show that the proposed algorithm is significantly more robust\nagainst noise and outliers comparing to other methods. The running time of the\nalgorithm is within a reasonable range for the solution of real-world\nclassification problems.","url_abs":"http://arxiv.org/abs/1804.09891v1","url_pdf":"http://arxiv.org/pdf/1804.09891v1.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":"optimal-margin-evolutionary-classifier","repo_url":"https://github.com/rezabonyadi/LinearOEC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"evolutionary-algorithms","task_name":"Evolutionary Algorithms"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}