Name
Abstract | ||
|---|---|---|
For classification problem, the training data will significantly influence the classification accuracy. However, the data in real-world applications often are imbalanced class distribution, that is, most of the data are in majority class and little data are in minority class. In this case, if all the data are used to be the training data, the classifier tends to predict that most of the incoming data belongs to the majority class. Hence, it is important to select the suitable training data for classification in the imbalanced class distribution problem. In this paper, we propose cluster-based under-sampling approaches for selecting the representative data as training data to improve the classification accuracy for minority class and investigate the effect of under-sampling methods in the imbalanced class distribution environment. The experimental results show that our cluster-based under-sampling approaches outperform the other under-sampling techniques in the previous studies. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.eswa.2008.06.108 | Expert Syst. Appl. |
Keywords | Field | DocType |
imbalanced class distribution environment,data mining,representative data,classification accuracy,imbalanced class distribution,incoming data,suitable training data,training data,minority class,classification,imbalanced data distribution,majority class,under-sampling,imbalanced class distribution problem,cluster-based under-sampling approach,classification data mining under-sampling imbalanced data distribution,distributed environment,sampling methods | Training set,Data mining,Computer science,Artificial intelligence,Sampling (statistics),Classifier (linguistics),Machine learning | Journal |
Volume | Issue | ISSN |
36 | 3 | Expert Systems With Applications |
Citations | PageRank | References |
135 | 3.56 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Show-Jane Yen | 1 | 537 | 130.05 |
| Yue-Shi Lee | 2 | 543 | 41.14 |