A hybrid classifier based on SVM method for cancer classification

Weronika Piątkowska, Jerzy Martyna

Abstract


In this paper, we proposed a new method of applying Support Vector Machines (SVMs) for cancer classification. We proposed a hybrid classifier that considers the degree of a membership function of each class with the help of Fuzzy Naive Bayes (FNB) and then organizes one-versus-rest (OVR) SVMs as the archi¬tecture classifying into the corresponding class. In this method, we used a novel system of ordering the recognized expression profiles by means of using FNB and genering SVMs with the OVR scheme. The results show that our hybrid classifier is comparable to the conventional methods.

Keywords


SVM method; Fuzzy Naive Bayes; cancer classification

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References


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DOI: http://dx.doi.org/10.21936/si2009_v30.n2A.492