Classification of Butterfly's Sub Families System using Fuzzy Bayesian Decision Method

Abstract

Data Mining refers to extracting or mining knowledge from large amounts of data. Classification is an important technique in data mining. Classification is the process of finding a set of models that describe and distinguish data classes, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. In this paper, Fuzzy Bayesian Classifier is one of simplest probabilistic classifiers. It is based on Bayes Theorem. A fuzzy logic based methodology for classification is developed and used in supervised learning. In this paper, Fuzzy Bay esian is used to build a classifier using a set of butterfly training dataset and to test the unknown dataset. The evaluation of the performance of the classifier is based on the feature set by using the hold out method as the evaluation criteria. The expe riment is performed on sub family of butterfly dataset from UC I rvine Repository of Machine Learning Databases.