SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Feature Reduction for Neural Network Based Text Categorization
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
Ontology-based Classification of Email
ITCC '03 Proceedings of the International Conference on Information Technology: Computers and Communications
A Neural Network Based Approach to Automated E-Mail Classification
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
An immunological filter for spam
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Neural recognition and genetic features selection for robust detection of e-mail spam
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
SVM classifier incorporating feature selection using GA for spam detection
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
Immune-Based peer-to-peer model for anti-spam
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Deterministic convergence of an online gradient method for BP neural networks
IEEE Transactions on Neural Networks
A hybrid particle swarm optimization approach for clustering and classification of datasets
Knowledge-Based Systems
A new feature selection algorithm based on binomial hypothesis testing for spam filtering
Knowledge-Based Systems
A novel virtual sample generation method based on Gaussian distribution
Knowledge-Based Systems
WSEAS Transactions on Information Science and Applications
Credit risk assessment and decision making by a fusion approach
Knowledge-Based Systems
A novel probabilistic feature selection method for text classification
Knowledge-Based Systems
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Email is one of the most ubiquitous and pervasive applications used on a daily basis by millions of people worldwide, individuals and organizations more and more rely on the emails to communicate and share information and knowledge. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. It is becoming a big challenge to process and manage the emails efficiently for and individuals and organizations. This paper proposes new email classification models using a linear neural network trained by perceptron learning algorithm and a nonlinear neural network trained by back-propagation learning algorithm. An efficient semantic feature space (SFS) method is introduced in these classification models. The traditional back-propagation neural network (BPNN) has slow learning speed and is prone to trap into a local minimum, so the modified back-propagation neural network (MBPNN) is presented to overcome these limitations. The vector space model based email classification system suffers from a large number of features and ambiguity in the meaning of terms, which will lead to sparse and noisy feature space. So we use the SFS to convert the original sparse and noisy feature space to a semantically richer feature space, which will helps to accelerate the learning speed. The experiments are conducted based on different training set size and extracted feature size. Experimental results show that the models using MBPNN outperform the traditional BPNN, and the use of SFS can greatly reduce the feature dimensionality and improve email classification performance.