Constructing Multiple Support Vector Machines Ensemble Based on Fuzzy Integral and Rough Reducts
| Autor: | Chun-Mei Liu, Qing-Lei Hu, Liang-Kuan Zhu, Yi-Zhuo Zhang |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Computer Science::Machine Learning
Boosting (machine learning) business.industry Probabilistic logic Pattern recognition Fuzzy control system Machine learning computer.software_genre Fuzzy logic Support vector machine Statistics::Machine Learning ComputingMethodologies_PATTERNRECOGNITION Computer Science::Sound Computer Science::Computer Vision and Pattern Recognition Classification result Ranking SVM Artificial intelligence business Intelligent control computer Mathematics |
| Zdroj: | 2007 2nd IEEE Conference on Industrial Electronics and Applications. |
| DOI: | 10.1109/iciea.2007.4318607 |
| Popis: | Even the multiple support vector machine (SVM) ensemble has been proved to improve the classification performance greatly than a single SVM, the classification result of the practically implemented SVM is often far from the theoretically expected level. As compared to traditional bagging and boosting methods, this paper proposes a novel SVM ensemble method based on fuzzy integral and rough reducts. In general, the proposed method is built in 3 steps: construct the individual SVM of ensemble by rough reduction technique; obtain the probabilistic outputs model of each component SVM; combine the component predictions based on fuzzy integral. The trained individual SVMs are aggregated to make a final decision. The simulating results demonstrate that the proposed multiple SVM ensemble method outperforms a single SVM and traditional SVM ensemble technique via bagging and boosting in terms of classification accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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