Image semantic annotation using fuzzy decision trees

One of the methods most commonly used for learning and classification is using decision trees. The greatest advantages that decision trees offer is that, unlike classical trees, they provide a support for handling uncertain data sets. The paper introduces a new algorithm for building fuzzy decision trees and also offers some comparative results, by taking into account other methods. We will present a general overview of the fuzzy decision trees and focus afterwards on the newly introduced algorithm, pointing out that it can be a very useful tool in processing fuzzy data sets by offering good comparative results.