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.
