Name
Title | ||
|---|---|---|
Boosting the Performance of the Fuzzy Min-Max Neural Network in Pattern Classification Tasks |
Abstract | ||
|---|---|---|
In this paper, a boosted Fuzzy Min-Max Neural Network (FMM) is proposed. While FMM is a learning algorithm which is able to learn new classes and to refine existing classes incrementally, boosting is a general method for improving accuracy of any learning algorithm. In this work, AdaBoost is applied to improve the performance of FMM when its classification results deteriorate from a perfect score. Two benchmark databases are used to assess the applicability of boosted FMM, and the results are compared with those from other approaches. In addition, a medical diagnosis task is employed to assess the effectiveness of boosted FMM in a real application. All the experimental results consistently demonstrate that the performance of FMM can be considerably improved when boosting is deployed. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/3-540-31662-0_29 | APPLIED SOFT COMPUTING TECHNOLOGIES: THE CHALLENGE OF COMPLEXITY |
Keywords | DocType | Volume |
pattern classification,fuzzy min-max neural network,AdaBoost | Conference | 34.0 |
ISSN | Citations | PageRank |
1615-3871 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kok Yeng Chen | 1 | 13 | 1.52 |
| Chee Peng Lim | 2 | 1459 | 122.04 |
| Robert F. Harrison | 3 | 277 | 29.20 |