Unification of Fuzzy {SVMs} and Rule Extraction Methods through imprecise Domain Knowledge
| Type of publication: | Inproceedings |
| Citation: | moewes_unification_2008 |
| Booktitle: | Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-08) |
| Year: | 2008 |
| Month: | June |
| Pages: | 1527--1534 |
| Location: | Torremolinos (Málaga) |
| Organization: | University of Málaga |
| URL: | http://www.gimac.uma.es/ipmu08... |
| Abstract: | In this paper, we want to motivate the combination of kernel-based methods with fuzzy rule extraction methods to describe uncertain domains by fuzzy models. We thus introduce and motivate the concept of a fuzzy support vector machine (FSVM) to incorporate impreciseness into kernel machines. Furthermore, we present the idea of a positive definite fuzzy classifier (PDFC), the rules of which are obtained by kernel-based models. We conclude with two vague conceptions to associate FSVM with PDFC to finally obtain understandable and meaningful fuzzy rules. |
| Keywords: | Binary Classification, Fuzzy Rules, fuzzy SVM, Support Vector Machine |
| Authors | |
| Editors | |
| Added by: | [ADM] |
| Total mark: | 0 |
|
Attachments
|
|
|
|
|
Notes
|
|
|
|
|
|
Topics
|
|
