Summary
The current study presents two approaches to the fuzzy support vector machine. The first approach implements the fuzzy support vector machine for solving a two class problem. The second approach employs the fuzzy support vector machine for a multi-class problem. In both cases fuzzy classifiers have been used for genes expression data analysis. The first method has been tested on clinical data acquired at the Silesian Medical University. Then the dataset from Kent Ridge Biomedical Data Set Repository has been used to simulate the performance of the second tool.
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Musioł, J., Więcławek, A., Mazurek, U. (2008). Fuzzy Support Vector Machine for Genes Expression Data Analysis. In: Pietka, E., Kawa, J. (eds) Information Technologies in Biomedicine. Advances in Soft Computing, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68168-7_43
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DOI: https://doi.org/10.1007/978-3-540-68168-7_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68167-0
Online ISBN: 978-3-540-68168-7
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