Study of Improving Support Vector Machine Algorithm Based on Medical Data Mining

Abstract:

This paper introduces fundamental theory and mathematic model of Support Vector Machine(SVM), and also covers applying SVM algorithm in data assorting. In conventional SVM model, sample set always has noisy and isolated points, for solving this problem this paper proposes a SVM boundary sample cut algorithm: first, pre-select boundary samples, then apply Remove-Only algorithm to remove some inappropriate points, then the result will be final sample set for SVM. At last, we compared conventional and improved algorithms by applying them on categorizing two medical data sets; the accuracy of improved algorithm achieves 100%. The result shows this improved algorithm is with significant practical advantage and value.

• Periodical: Advanced Materials Research (Volumes 532-533)
• Pages: 1780-1784
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.532-533.1780
• Online since: June 2012
• Authors: Wei Wu
• Keywords: Boundary Sample, Data Mining (DM), Support Vector Machine (SVM)

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