Name
Title | ||
|---|---|---|
Adaptive Ensemble Method Based on Spatial Characteristics for Classifying Imbalanced Data |
Abstract | ||
|---|---|---|
AbstractThe class imbalance problems often reduce the classification performance of the majority of standard classifiers. Many methods have been developed to solve these problems, such as cost-sensitive learning methods, synthetic minority oversampling technique (SMOTE), and random oversampling (ROS). However, the existing methods still have some problems due to the possible performance loss of useful information and overfitting. To solve the problems, we propose an adaptive ensemble method by using the most advanced feature of self-adaption by considering an average Euclidean distance between test data and training data, where the average distance is calculated by k-nearest neighbors (KNN) algorithm. Simulation results are provided to confirm that the proposed method has a better performance than existing ensemble methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1155/2017/3704525 | Periodicals |
Field | DocType | Volume |
Training set,Oversampling,Pattern recognition,Computer science,Euclidean distance,Speech recognition,Imbalance problems,Artificial intelligence,Test data,Overfitting,Ensemble learning | Journal | 2017 |
Issue | ISSN | Citations |
1 | 1058-9244 | 0 |
PageRank | References | Authors |
0.34 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Wang | 1 | 43 | 4.20 |
| Lei Zhao | 2 | 30 | 16.56 |
| Guan Gui | 3 | 641 | 102.53 |
| Baoyu Zheng | 4 | 1008 | 82.73 |
| Ruochen Huang | 5 | 8 | 1.93 |