Authors:
Paulo Cortez
1
;
Rui Vaz
1
;
Miguel Rocha
1
;
Miguel Rio
2
and
Pedro Sousa
1
Affiliations:
1
Universidade do Minho, Portugal
;
2
University College London, United Kingdom
Keyword(s):
Collaborative Filtering, Content-based Filtering, Evolutionary Algorithms, Feature Selection, Naive Bayes, Spam Email, Symbiotic Filtering, Text Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computation and Control
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
;
Soft Computing
Abstract:
This work presents a symbiotic filtering approach enabling the exchange of relevant word features among different users in order to improve local anti-spam filters. The local spam filtering is based on a Content-Based Filtering strategy, where word frequencies are fed into a Naive Bayes learner. Several Evolutionary Algorithms are explored for feature selection, including the proposed symbiotic exchange of the most relevant features among different users. The experiments were conducted using a novel corpus based on the well known Enron datasets mixed with recent spam. The obtained results show that the symbiotic approach is competitive.