An Improved Weighted Naive Bayes Classification Algorithm Using Feature Correlation
Dongzhan Zhang · Journal of Xiamen University · 2012
The strong independence condition between the feature required by naive Bayes classification algorithm is very difficult to realize in reality.This paper puts forward an improved weighted naive naive Bayes classification algorithm using feature correlation to loose this condition to some extent,this algorithm adopts a new weighting method called TF-IDF-FC weight calculation,it takes into account the feature distribution within and between class based on the traditional TF-IDF weight calculation method and adjusts feature weight in combination with feature correlation in order to make the weight of the feature which can represent its class mostly.Compared with weighted naive Bayes classification based on the traditional TF-IDF weight and other commonly used weighted naive Bayes classification algorithms,such as attribute weighted naive Bayes classification,this algorithm improve the performance of classification to a certain extent.