Study on Ensemble Classification Methods towards Spam Filtering

Authors:
Jinlong Wang;Ke Gao;Yang Jiao;Gang Li
Affiliations:
School of Computer Engineering, Qingdao Technological University, Qingdao, China 266033;School of Computer Engineering, Qingdao Technological University, Qingdao, China 266033;School of Computer Engineering, Qingdao Technological University, Qingdao, China 266033;School of Information Technology, Deakin University, Victoria, Australia 3125
Venue:
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Year:
2009

Citing 13
Cited 0

Making large-scale support vector machine learning practical

Advances in kernel methods
Ensembling neural networks: many could be better than all

Artificial Intelligence
Ensemble Methods in Machine Learning

MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
An evaluation of statistical spam filtering techniques

ACM Transactions on Asian Language Information Processing (TALIP)
On-line spam filter fusion

SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Spam and the ongoing battle for the inbox

Communications of the ACM - Spam and the ongoing battle for the inbox
Spam Filtering Using Statistical Data Compression Models

The Journal of Machine Learning Research
Relaxed online SVMs for spam filtering

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A comparative study for content-based dynamic spam classification using four machine learning algorithms

Knowledge-Based Systems
Email Spam Filtering: A Systematic Review

Foundations and Trends in Information Retrieval
Targeting spam control on middleboxes: Spam detection based on layer-3 e-mail content classification

Computer Networks: The International Journal of Computer and Telecommunications Networking
Review: A review of machine learning approaches to Spam filtering

Expert Systems with Applications: An International Journal
Support vector machines for spam categorization

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

Recently, many scholars make use of fusion of filters to enhance the performance of spam filtering. In the past several years, a lot of effort has been devoted to different ensemble methods to achieve better performance. In reality, how to select appropriate ensemble methods towards spam filtering is an unsolved problem. In this paper, we investigate this problem through designing a framework to compare the performances among various ensemble methods. It is helpful for researchers to fight spam email more effectively in applied systems. The experimental results indicate that online based methods perform well on accuracy, while the off-line batch methods are evidently influenced by the size of data set. When a large data set is involved, the performance of off-line batch methods is not at par with online methods, and in the framework of online methods, the performance of parallel ensemble is better when using complex filters only.