A Comparative Impact Study of Attribute Selection Techniques on Naïve Bayes Spam Filters

Authors:
J. R. Méndez;I. Cid;D. Glez-Peña;M. Rocha;F. Fdez-Riverola
Affiliations:
Dept. Informática, University of Vigo, Escuela Superior de Ingeniería Informática Edificio Politécnico, Ourense, Spain 32004;Dept. Informática, University of Vigo, Escuela Superior de Ingeniería Informática Edificio Politécnico, Ourense, Spain 32004;Dept. Informática, University of Vigo, Escuela Superior de Ingeniería Informática Edificio Politécnico, Ourense, Spain 32004;Dept. Informática, University of Minho, Centro de Ciências e Tecnologias da Computação., Braga, Portugal 4710-057;Dept. Informática, University of Vigo, Escuela Superior de Ingeniería Informática Edificio Politécnico, Ourense, Spain 32004
Venue:
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Year:
2008

Citing 7
Cited 4

Performance standards and evaluations in IR test collections: cluster-based retrieval models

Information Processing and Management: an International Journal
Feature Selection: Evaluation, Application, and Small Sample Performance

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparative Study on Feature Selection in Text Categorization

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A comparison of event models for Naive Bayes anti-spam e-mail filtering

EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Applying lazy learning algorithms to tackle concept drift in spam filtering

Expert Systems with Applications: An International Journal
A study of cross-validation and bootstrap for accuracy estimation and model selection

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Estimating continuous distributions in Bayesian classifiers

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

The Impact of Noise in Spam Filtering: A Case Study

ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Symbiotic filtering for spam email detection

Expert Systems with Applications: An International Journal
Rough sets for spam filtering: Selecting appropriate decision rules for boundary e-mail classification

Applied Soft Computing
Grindstone4Spam: An optimization toolkit for boosting e-mail classification

Journal of Systems and Software

Quantified Score

Hi-index	0.00

Visualization

Abstract

The main problem of Internet e-mail service is the massive spam message delivery. Everyday, millions of unwanted and unhelpful messages are received by Internet users annoying their mailboxes. Fortunately, nowadays there are different kinds of filters able to automatically identify and delete most of these messages. In order to reduce the bulk of information to deal with, only distinctive attributes are selected spam and legitimate e-mails. This work presents a comparative study about the performance of five well-known feature selection techniques when they are applied in conjunction with four different types of Naïve Bayes classifier. The results obtained from the experiments carried out show the relevance of choosing an appropriate feature selection technique in order to obtain the most accurate results.