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In medical applications such as recognizing the type of a tumor as Malignantor Benign, a wrong diagnosis can be devastating. Methods like Fuzzy SupportVector Machines (FSVM) try to reduce the effect of misplaced training points byassigning a lower weight to the outliers. However, there are still uncertainpoints which are similar to both classes and assigning a class by the giveninformation will cause errors. In this paper, we propose a two-phaseclassification method which probabilistically assigns the uncertain points toeach of the classes. The proposed method is applied to the Breast CancerWisconsin (Diagnostic) Dataset which consists of 569 instances in 2 classes ofMalignant and Benign. This method assigns certain instances to theirappropriate classes with probability of one, and the uncertain instances toeach of the classes with associated probabilities. Therefore, based on thedegree of uncertainty, doctors can suggest further examinations before makingthe final diagnosis.

Probabilistic Classification using Fuzzy Support Vector Machines