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Erschienen in:
2007 | OriginalPaper | Buchkapitel
An Improved SVM Classifier for Medical Image Classification
verfasst von : Yun Jiang, Zhanhuai Li, Longbo Zhang, Peng Sun
Erschienen in: Rough Sets and Intelligent Systems Paradigms
Verlag: Springer Berlin Heidelberg
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Support Vector Machine (SVM) has high classifying accuracy and good capabilities of fault-tolerance and generalization. The Rough Set Theory (RST) approach has the advantages on dealing with a large amount of data and eliminating redundant information. In this paper, we join SVM classifier with RST which we call the Improved Support Vector Machine (ISVM) to classify digital mammography. The experimental results show that this ISVM classifier can get 96.56% accuracy which is higher about 3.42% than 92.94% using SVM, and the error recognition rates are close to 100% averagely.