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

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. We give a summary of previous improvement methods of the NBC model. In our study, 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 and the relevance between features. This strategy overcomes deficiencies caused by the assumption of independence. Through the experimental comparison and analysis on the UCI datasets, the strategy is proved effective