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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kocyigit, Y. Seker, H. |
| Copyright Year | 2012 |
| Description | Author affiliation: Bio-Health Informatics Research Group, Faculty of Technology, De Montfort University, Leicester, LE1 9BH, UK (Kocyigit, Y.; Seker, H.) |
| Abstract | Pattern classifiers developed with the imbalanced data set tend to classify an object to the class with the highest number of samples, resulting in higher overall classifier accuracy but lower sensitivity. A new approach based on a dynamic under-sampling procedure is therefore proposed to improve the classification of imbalanced datasets that are quite common in bio-medicine. To overcome a class imbalance, the dataset is resampled by using the ensemble fuzzy c-means clustering method. The under-sampling procedure is then applied to the majority class to balance the size of the classes. Compared to the existing classifiers, the proposed method yields not only higher classification accuracy and sensitivity but also more stable classification performance under different data sets, classifiers and their parameters, indicating that it is independent of particular clustering or classification methods. |
| Starting Page | 952 |
| Ending Page | 955 |
| File Size | 691318 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457721762 |
| e-ISBN | 9781457721779 |
| e-ISBN | 9781457721755 |
| DOI | 10.1109/BHI.2012.6211746 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-01-05 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Biological system modeling Diabetes |
| Content Type | Text |
| Resource Type | Article |
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