Abstract
In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.
| Original language | English |
|---|---|
| Pages (from-to) | 2841-2847 |
| Number of pages | 7 |
| Journal | Journal of Medical Systems |
| Volume | 36 |
| Issue number | 5 |
| DOIs | |
| State | Published - Oct 2012 |
Keywords
- Ensemble learning; Neural fuzzy; KNN; Quadratic classifier; Information gain
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Cite this
Hsieh, S-L., Hsieh, S-H., Cheng, P-H., Chen, C-H., Hsu, K-P., Lee, I-S., Wang, Z., & Lai, F-P. (2012). Design Ensemble Machine Learning Model for Breast Cancer Diagnosis. Journal of Medical Systems, 36(5), 2841-2847. https://doi.org/10.1007/s10916-011-9762-6