- Title
- Isolation kernel and its effect on SVM
- Creator
- Ting, Kaiming; Zhu, Yue; Zhou, Zhi-Hua
- Date
- 2018
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/167662
- Identifier
- vital:13711
- Identifier
-
https://doi.org/10.1145/3219819.3219990
- Identifier
- ISBN:978-1-4503-5552-0
- 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.
- Publisher
- Association for Computing Machinery
- Relation
- 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018; London, United Kingdom; 19th-23th August 2018 p. 2329-2337
- Rights
- Copyright © 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Data dependent kernel; SVM classifiers; Random Forest; Isolation Forest
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