Name
Title | ||
|---|---|---|
A k-means-based and no-super-parametric Improvement of AdaBoost and its Application to Transaction Fraud Detection |
Abstract | ||
|---|---|---|
AdaBoost is a well-known effective boosting algorithm for classification and has achieved successful applications in many fields. The existing studies show that it is very sensitive to noisy points, resulting in a decline of classification performance. We have proposed an improved algorithm called CAdaBoost in order to overcome the weakness. However, our CAdaBoost uses a set of super-parameters. In this paper, we propose a no-super-parametric improvement to CAdaBoost and it is applied to the problem of detecting credit card fraud. Although the performance of this CAdaBoost without super-parameters is a little worse than the original CAdaBoost, it still outperforms others including the original AdaBoost and several existing improvements of AdaBoost. Our design without super-parameters provides a helpful idea for other similar problems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICNSC48988.2020.9238121 | 2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) |
Keywords | DocType | ISBN |
Ensemble Learning,AdaBoost,k-means,transaction fraud detection | Conference | 978-1-7281-6856-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Fan Yang | 1 | 0 | 0.34 |
| GuanJun Liu | 2 | 176 | 26.24 |
| Chun-Gang Yan | 3 | 62 | 15.97 |