Name
Title | ||
|---|---|---|
Stochastic Sensitivity Measure-Based Noise Filtering and Oversampling Method for Imbalanced Classification Problems |
Abstract | ||
|---|---|---|
Class imbalance problems occur in many real-world applications. Oversampling methods are effective to handle class imbalance issues by replicating or generating new minority samples to rebalance the class distribution. However, current methods directly using all minority samples will also use noisy samples to generate new samples which may lead to more severe class overlapping and introduce more noisy samples. In this work, we propose a stochastic sensitivity measure-based noise filtering and oversampling method, i.e. the SSMNFOS, to improve the robustness of oversampling method with respect to noisy samples. Samples yielding high stochastic sensitivities are identified as noises by a neural network ensemble and will not participate in the oversampling method for rebalancing the class distribution. Comprehensive experimental studies are carried out on ten datasets with five different noise levels to analyze the effectiveness of the proposed method. Experimental results show that the SSMNFOS outperforms state-of-the-art methods with 95% statistical significance. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SMC.2018.00078 | 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
Keywords | Field | DocType |
stochastic sensitivity measure,imbalanced learning,noise filtering,oversampling | Pattern recognition,Oversampling,Computer science,Filter (signal processing),Robustness (computer science),Imbalance problems,Artificial intelligence,Artificial neural network,Machine learning | Conference |
ISSN | ISBN | Citations |
1062-922X | 978-1-5386-6651-7 | 1 |
PageRank | References | Authors |
0.35 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianjun Zhang | 1 | 9 | 3.48 |
| Wing W. Y. Ng | 2 | 528 | 56.12 |