Document security identification based on multi-classifier

Abstract

Data leakage is a potentially important issue for businesses. Numerous corporate offer data loss prevention (DLP) solutions to monitor information flow, and detect such leakage. Adding a secret label to a document, DLP can use documents label to do securely control, effectively protecting data. With the increasing documents every day, manual labeling is time-consuming. To better solve the difficult task, recently researchers need to start use document security identification by machine learning quickly classify a large number of texts. The contribution of this paper is to explore dimensionality reduction by feature selection and combine two models to avoid the process of weighting different type of features. In contrast to training all features with one algorithm, our experimental results demonstrate that the combination of two models can improve the classification performance.