Publications (3)0 Total impact
Conference Paper: Gender Identification for Chinese E-mail documents[Show abstract] [Hide abstract]
ABSTRACT: In this paper, the method of gender identification for Chinese e-mail documents is described. E-mail documents' features including linguistic features, format features and structure features were analyzed. The support vector machine algorithm was selected as classification algorithm. Experiments on a set of samples gave promising results, which proved that the method was feasible.
Conference Paper: Approach to verify new class in the classification process[Show abstract] [Hide abstract]
ABSTRACT: Support vector machine (SVM) is a binary class machine learning classifier. Given a data point, the SVM can classify the data point to either positive class or negative class. However, in some cases, some data points belong to neither positive class nor negative class. They should be treated as one new class. This paper proposes one method that can find isolated data points and separate them into new classes based on F-test and the experimental results show that the method is effective.
Conference Paper: E-mail authorship mining based on SVM for computer forensic[Show abstract] [Hide abstract]
ABSTRACT: We describe our work which attempts to mine e-mail authorship for the purpose of computer forensic. We extract various e-mail document features including linguistic features, header features and structural characteristics. These features together are used with the support vector machine learning algorithm to classify or attribute authorship of e-mail messages to an author. The primary experiments on a number of e-mail documents have given ideal results, which indicate that the project has laid a firm groundwork for the future work.
Hebei UniversityPao-ting-shih, Hebei, China