On Effective E-mail Classification via Neural Networks

• Published in: Database and Expert Systems Applications pp. 85 - 94
• Main Authors: Cui, Bin, Mondal, Anirban, Shen, Jialie, Cong, Gao, Tan, Kian-Lee
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Information systems. Data bases; Memory organisation. Data processing; Software; Performance evaluation; Free field; Dimensionality; Model-based reasoning; Filtering; Text; Electronic mail; Neural network; Modeling; Semantics; Semistructured data; Classification; Database; Internet; Preprocessor; Artificial intelligence; Principal component analysis
• ISBN: 3540285660; 9783540285663
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11546924_9

For addressing the growing problem of junk E-mail on the Internet, this paper proposes an effective E-mail classifying and cleansing method in this paper. Incidentally, E-mail messages can be modelled as semi-structured documents consisting of a set of fields with pre-defined semantics and a number of variable length free-text fields. Our proposed method deals with both fields having pre-defined semantics as well as variable length free-text fields for obtaining higher accuracy. The main contributions of this work are two-fold. First, we present a new model based on the Neural Network (NN) for classifying personal E-mails. In particular, we treat E-mail files as a particular kind of plain text files, the implication being that our feature set is relatively large (since there are thousands of different terms in different E-mail files). Second, we propose the use of Principal Component Analysis (PCA) as a preprocessor of NN to reduce the data in terms of both size as well as dimensionality so that the input data become more classifiable and faster for the convergence of the training process used in the NN model. The results of our performance evaluation demonstrate that the proposed algorithm is indeed effective in performing filtering with reasonable accuracy.