SVM with multiple kernels based on manifold learning for Breast Cancer diagnosis
| Autor: | Mingrui Shi, Hui Peng, Xiufeng Yang |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
business.industry
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION information science Nonlinear dimensionality reduction Cancer Pattern recognition Machine learning computer.software_genre medicine.disease Support vector machine Set (abstract data type) ComputingMethodologies_PATTERNRECOGNITION Breast cancer Isometric feature mapping medicine Artificial intelligence skin and connective tissue diseases Breast cancer classification Isomap business computer |
| Zdroj: | ICIA |
| DOI: | 10.1109/icinfa.2013.6720330 |
| Popis: | In this paper, we propose an efficient algorithm Support Vector Machines with multiple kernels based on Isometric feature mapping(Isomap) in the process of breast cancer classification. We use Wisconsin Diagnostic Breast Cancer (WDBC) as our original data set. The first step, we use Isomap to project high dimensional breast cancer data into a much lower dimensional space. Second, we use SVM with multiple kernels to classify the lower dimensional breast cancer data. Finally, the experimental results illustrate that the proposed algorithm has a better performance than traditional SVM for breast cancer classification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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