The Classification Based on Evidence Theory in Medical Data Mining

Studies have revealed that a combination of classifiers is often more accurate than an individual classifier. In this paper we propose a new method for combination of multiple classifiers using Dempster-Shafer theory of evidence combination for mining medical data. We combine the beliefs of three classifiers:Decision Tree,K-Nearest Neighbor and Naïve Bayesian.Our experiments over the Wisconsion Breast Canser dataset shows that out approach has better accuracy than any individual classifier. In addition the performance of our suggested method is better than similar method and weighted linear and majority vote combination models.

The Classification Based on Evidence Theory in Medical Data Mining Keywords:

Medical Data Mining , Multiple Classification , Dempster-Shafer Theory , Wisconsion Breast Canser