Name
Title | ||
|---|---|---|
Optimizing Weighted Extreme Learning Machines for imbalanced classification and application to credit card fraud detection |
Abstract | ||
|---|---|---|
•This work applies a Weighted Extreme Learning Machine (WELM) to handle imbalanced classification problems.•This work proposes to apply various intelligent optimization methods to optimize a WELM and compare their performance in imbalanced classification data sets.•This work presents experimental results that show that WELM with a dandelion algorithm with probability-based mutation can perform better than WELM with improved particle swarm optimization, bat algorithm, genetic algorithm, dandelion algorithm and self-learning dandelion algorithm.•This work applies the proposed algorithms to credit card fraud detection-an important application field of imbalanced classification. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2020.04.078 | Neurocomputing |
Keywords | DocType | Volume |
Imbalanced classification,Weighted Extreme Learning Machine,Dandelion algorithm with probability-based mutation,Credit card fraud detection | Journal | 407 |
ISSN | Citations | PageRank |
0925-2312 | 1 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Honghao Zhu | 1 | 1 | 0.34 |
| GuanJun Liu | 2 | 176 | 26.24 |
| MengChu Zhou | 3 | 8989 | 534.94 |
| Yu Xie | 4 | 10 | 5.81 |
| Abdullah Abusorrah | 5 | 121 | 17.75 |
| Qi Kang | 6 | 13 | 4.23 |