A rough set-based hybrid feature selection method for topic-specific text filtering

With the proliferation of harmful Internet content such as pornography, violence, and hate messages, effective content-filtering systems are essential. However, a non-trivial obstacle in good text filtering is the high dimensionality of the data. We introduced a hybrid method to select features more accurately using some feature selection method and rough set theory. We can select features firstly using one of feature selection methods, such as x/sup 2/ statistic, mutual information, information gain, and then further select features using rough set. Thus more accurate and less features are extracted. In experiments, we used UCI machine learning dataset as our dataset. We use naive Bayes model to evaluate our feature selection method, the result shows our method has high precision and high recall, and is very effective and efficient.