Business email classification using incremental subspace learning

We consider a new text classification task: classifying enterprise email messages into sensitive business topics. The identification of sensitive topics in email messages is important for enterprises to safeguard their critical data such as intellectual properties and trade secrets. We introduce the incremental PCA (Principal Component Analysis) to email representation, which can learn a feature subspace incrementally and effectively to reduce the feature dimensionality. Linear SVM (Support Vector Machine) is then adopted to learn the classification models. We validate our approaches with 5,000 emails extracted from the Enron Email set. Experimental results show that SVM outperforms other classification methods, and the incremental PCA produces a substantial reduction in the processing time and a slight increase in the classification accuracy compared to SVM with all the features.