An Improved Native Bayes Classifier for Imbalanced Text Categorization Based on K-Means and Chi-Square Feature Selection

In multiclass text classification, the performance of classifier is usually very low while the data distribution is very uneven. The reason may be that the majority class has a great influence on classifier, and the minority class ignored. This study attempts to solve this problem by improving CHI-square feature selection algorithm and by improving the Term Frequency-Inverse Document Frequency (TF-IDF) weight algorithm and combining with the K-means clustering method, generating minority class samples to reduce the influence of inclined class distribution. The experimental results demonstrate that the improved method has a significant improvement on the classification efficiency on the bias dataset.