{"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/isolation-kernel-and-its-effect-on-svm","title":"Isolation Kernel and Its Effect on SVM","arxiv_id":null,"date":"2018-07-19","proceeding":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 7","authors":["Kai Ming Ting","Yue Zhu","Zhi-Hua Zhou"],"abstract":"This paper investigates data dependent kernels that are derived directly from data. This has been an outstanding issue for about two decades which hampered the development of kernel-based methods. We introduce Isolation Kernel which is solely dependent on data distribution, requiring neither class information nor explicit learning to be a classifier. In contrast, existing data dependent kernels rely heavily on class information and explicit learning to produce a classifier. We show that Isolation Kernel approximates well to a data independent kernel function called Laplacian kernel under uniform density distribution. With this revelation, Isolation Kernel can be viewed as a data dependent kernel that adapts a data independent kernel to the structure of a dataset. We also provide a reason why the proposed new data dependent kernel enables SVM (which employs a kernel through other means) to improve its predictive accuracy. The key differences between Random Forest kernel and Isolation Kernel are discussed to examine the reasons why the latter is a more successful tree-based kernel.","url_abs":"https://dl.acm.org/doi/abs/10.1145/3219819.3219990","url_pdf":"https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/kdd18.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":"isolation-kernel-and-its-effect-on-svm","repo_url":"https://github.com/david-cortes/isotree","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"BSD-2-Clause"}}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"classification","task_name":"General 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}