Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Evolutionary learning of document categories
Information Retrieval
Statistical recognition of noun phrases in unrestricted text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
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This paper presents a system for classifying e-mails into two categories, legitimate and fraudulent. This classifier system is based on the serial application of three filters: a Bayesian filter that classifies the textual content of e-mails, a rule based filter that classifies the nongrammatical content of e-mails and, finally, a filter based on an emulator of fictitious accesses which classifies the responses from websites referenced by links contained in e-mails. The approach of this system is hybrid, because it uses different classification methods, and also integrated, because it takes into account all kind of data and information contained in e-mails.