Improving the convergence of the back-propagation algorithm
Neural Networks
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Information Retrieval
Training Neural Networks with Threshold Activation Functions and Constrained Integer Weights
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
A Neural Network Based Approach to Automated E-Mail Classification
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Reformulation of queries using similarity thesauri
Information Processing and Management: an International Journal
Hierarchical document categorization with k-NN and concept-based thesauri
Information Processing and Management: an International Journal
Using online linear classifiers to filter spam emails
Pattern Analysis & Applications
Applying lazy learning algorithms to tackle concept drift in spam filtering
Expert Systems with Applications: An International Journal
SpamHunting: An instance-based reasoning system for spam labelling and filtering
Decision Support Systems
A neural model in anti-spam systems
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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
On effective e-mail classification via neural networks
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
Query expansion with an automatically generated thesaurus
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
A unified approach on fast training of feedforward and recurrentnetworks using EM algorithm
IEEE Transactions on Signal Processing
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Deterministic convergence of an online gradient method for BP neural networks
IEEE Transactions on Neural Networks
Word sense disambiguation for spam filtering
Electronic Commerce Research and Applications
Automatic thesaurus construction for cross generation corpus
Journal on Computing and Cultural Heritage (JOCCH)
An incremental construction method of a large-scale thesaurus using co-occurrence information
International Journal of Computer Applications in Technology
| Hi-index | 12.05 |
Email has become one of the fastest and most economical forms of communication. Email is also one of the most ubiquitous and pervasive applications used on a daily basis by millions of people worldwide. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. This paper proposes a new spam filtering system using revised back propagation (RBP) neural network and automatic thesaurus construction. The conventional back propagation (BP) neural network has slow learning speed and is prone to trap into a local minimum, so it will lead to poor performance and efficiency. The authors present in this paper the RBP neural network to overcome the limitations of the conventional BP neural network. A well constructed thesaurus has been recognized as a valuable tool in the effective operation of text classification, it can also overcome the problems in keyword-based spam filters which ignore the relationship between words. The authors conduct the experiments on Ling-Spam corpus. Experimental results show that the proposed spam filtering system is able to achieve higher performance, especially for the combination of RBP neural network and automatic thesaurus construction.