Probabilistic Classification using Fuzzy Support Vector Machines

TL;DR

This paper introduces a probabilistic extension to Fuzzy Support Vector Machines for medical diagnosis, enabling better handling of uncertain data points by assigning probabilities to class memberships, thus aiding clinical decision-making.

Contribution

It proposes a novel two-phase classification method that probabilistically classifies uncertain points, improving diagnostic accuracy in medical datasets compared to traditional FSVM.

Findings

Accurately classifies certain instances with probability one.

Assigns probabilistic memberships to uncertain instances.

Enhances decision support for medical diagnosis.

Abstract

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.