Author Identification of E-mail Messages with OLMAM Trained Feedforward Neural Networks

The OLMAM algorithm (optimized Levenberg-Marquardt with adaptive momentum) is a variant of the Levenberg-Marquardt algorithm for training multilayer feedforward neural networks. OLMAM has been shown to obtain excellent solutions in difficult classification problems where other computational intelligence techniques usually achieve inferior performances. In this paper we apply OLMAM to the problem of author identification of e-mail messages which is a challenging classification problem due to the special characteristics of the data. We performed a number of experiments with a corpus of real-world e-mail messages (Enron corpus). The performance of the proposed method was compared with the performances achieved by Naive-Bayes and SVM classifiers. Author identification with OLMAM was found to be significantly better compared with the other methods even if the author wrote about different topics.