Disadvantages Of Naive Bayes Classifier
Naive Bayes Classifier
A Naïve Bayes [2]Classifier is a simple probalistic classifier based on applying Bayes theorem having strong(Naive) independence assumptions. A Naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature. Naive Bayes Classifiers can be trained very efficiently in a supervised learning setting because they depend on the probability model.This format is very much ambiguous for requirement specifications so it is hard to identify consistencies. A method is used for requirements specifications documents having similar contents to each other through a hierarchical text classification. This method has two main classification process: heavy classification and light classification. Heavy classification is used for Naïve Bayes(based on probalistic classification) while the Light classification process is used to elaborate specification requirement documents by using Euclidean Distance[4].
Advantages: 1.The Naive Bayes classifier is a popular machine learning method for text classification because performs well.
It is fast easy to implement .
Limitations:
1. Naive- Bayes is used to handle only low size. The classifier will pick the highest likelihood category as the one to which the document is annoyed too.
2.Back propagation Neural Network: The back transmission neural network is used for multi-layer feed- onward neural network with
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