Study on Naive Bayesian Classifier and its relation to Information Gain

Abstract

Classification and clustering techniques in d ata mining are useful for a wide variety of real time applications dealing with large amount o f data. Some of the application areas of data mining are text classification, medical diagnosis, intrusion detection systems etc . The Naive Bayes Classifier techn ique is based on the Bayesian theorem and is particularly suited when the dimensionality of the inputs is high. Despite its simplicity, Naive Bayes can often outperform more sophisticated classification methods. The approach is called "naïve" because it assumes the independence between the various attribute values. Naïve Bayes classification can be viewed as both a descriptive and a predictive type of algorithm. The probabilities are descriptive and are then used to predict the class membership for a untrained data.