Abstract
This paper presents new pruning and learning methods for the fuzzy rule-based classifier. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are adjusted by using the gradient descent method so that the misclassified feature vectors are correctly recategorized. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.
| Original language | English |
|---|---|
| Pages (from-to) | 416-425 |
| Number of pages | 10 |
| Journal | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
| Volume | 3613 |
| Issue number | PART I |
| DOIs | |
| Publication status | Published - 2005 |
| Event | Second International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China Duration: 2005 Aug 27 → 2005 Aug 29 |
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)