Name
Abstract | ||
|---|---|---|
Learning from imbalanced data is a challenging task in a wide range of applications, which attracts significant research efforts from machine learning and data mining community. As a natural approach to this issue, oversampling balances the training samples through replicating existing samples or synthesizing new samples. In general, synthesization outperforms replication by supplying additional information on the minority class. However, the additional information needs to follow the same normal distribution of the training set, which further constrains the new samples within the predefined range of training set. In this paper, we present the Wiener process oversampling (WPO) technique that brings the physics phenomena into sample synthesization. WPO constructs a robust decision region by expanding the attribute ranges in training set while keeping the same normal distribution. The satisfactory performance of WPO can be achieved with much lower computing complexity. In addition, by integrating WPO with ensemble learning, the WPOBoost algorithm outperformsmany prevalent imbalance learning solutions. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11704-016-5250-y | Frontiers of Computer Science |
Keywords | Field | DocType |
imbalanced-data learning,oversampling,ensemble learning,Wiener process,AdaBoost | Wiener process,Data mining,Normal distribution,Computer science,Natural approach,Artificial intelligence,Ensemble learning,Information needs,AdaBoost,Pattern recognition,Oversampling,Boosting (machine learning),Machine learning | Journal |
Volume | Issue | ISSN |
11 | 5 | 2095-2228 |
Citations | PageRank | References |
3 | 0.40 | 18 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qian Li | 1 | 32 | 8.29 |
| Gang Li | 2 | 381 | 62.77 |
| Wenjia Niu | 3 | 178 | 30.33 |
| Ya-nan Cao | 4 | 131 | 19.42 |
| Liang Chang | 5 | 118 | 34.68 |
| Jianlong Tan | 6 | 132 | 22.14 |
| Li Guo | 7 | 102 | 10.23 |