Probabilistic Classification using Fuzzy Support Vector Machines
This work addresses medical diagnosis errors by providing probabilistic classifications for uncertain cases, though it appears incremental as it builds on Fuzzy Support Vector Machines.
The paper tackles the problem of uncertain points in medical classification by proposing a two-phase method that probabilistically assigns uncertain instances to classes, applied to the Breast Cancer Wisconsin Dataset with 569 instances.
In medical applications such as recognizing the type of a tumor as Malignant or Benign, a wrong diagnosis can be devastating. Methods like Fuzzy Support Vector Machines (FSVM) try to reduce the effect of misplaced training points by assigning a lower weight to the outliers. However, there are still uncertain points which are similar to both classes and assigning a class by the given information will cause errors. In this paper, we propose a two-phase classification method which probabilistically assigns the uncertain points to each of the classes. The proposed method is applied to the Breast Cancer Wisconsin (Diagnostic) Dataset which consists of 569 instances in 2 classes of Malignant and Benign. This method assigns certain instances to their appropriate classes with probability of one, and the uncertain instances to each of the classes with associated probabilities. Therefore, based on the degree of uncertainty, doctors can suggest further examinations before making the final diagnosis.