Data Modeling with Type I and Type II Fuzzy Sets

The fuzzy classifier is an algorithm that assigns a class label to an object, based on the object description. It is also said that the classifier predicts the class label. The object description comes in the form of a vector containing values of the features (attributes) deemed to be relevant for the classification task. Typically, the classifier learns to predict class labels using a training algorithm and a training data set. When a training data set is not available, a classifier can be designed from prior knowledge and expertise. Once trained, the classifier is ready for operation on unseen objects.In this thesis, type-1fuzzy classifier, and the type-2 fuzzy classifier are used for the machine learning datasets classification. The Wisconsin breast cancer dataset, Iris Dataset, and Tic-Tac-Toe datasets are classified. Type-2 fuzzy classifiers are able to perform better than type-1 fuzzy classifiers which have additional design parameters. Therefore, type-2 fuzzy classifiers are more attractive than the type-1 classifiers. The essential benefits the type-2 fuzzy logic classifiers are their ability to handle more vagueness. Keywords: Classifier, Type-1fuzzy classifier, Type-2 fuzzy classifier, Machine learning dataset and Uncertainty.