Study of test classification algorithm based on domain knowledge

摘要

When facing various and massive data resources, how to effectively utilize the resources according to the division field is one of the core problem of the institutional repository research. In this paper, we improved Bayesian classification algorithm, then proposed a text classification algorithm based on domain knowledge. Furthermore, some key technologies such as text classification, feature selection, weight improvement and domain knowledge algorithm improvement are designed and implemented. We use widely applied IkAnalyzer method to classify Chinese words. For feature selection and weight improvement part, we focus on the processing of special vocabulary in the document. We introduce the field expand vocabulary assist the Bayesian formula in the field application part to obtain the final result. The experiment result shows that the improved algorithm enhanced the accuracy of the classification efficiently, and the system calculating time is acceptable.