The Improvement of Naive Bayesian Classifier Based on the Strategy of Fuzzy Feature Selection with the Dual Space

Naive Bayesian classifier (NBC) is a simple and effective classification model. However, the fact that the assumption of independence is often violated in reality makes it perform poorly on some datasets. In our pre-research, we attempt to improve the NBC model based on the strategy of the fuzzy feature selection. The main idea of the improvement strategy is to adjust the features´ contribution to classification through the feature important factor (FIF) which describes the importance of the features. This strategy overcomes deficiencies caused by the assumption of independence. Based on the pre-research, we optimize the strategy of fuzzy feature selection with the establishment of the dual NBC model in order to improve the NBC model more. Through the experimental analysis on the UCI datasets, the strategy of the fuzzy feature selection on the dual NBC model is proved effective.