A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Communications of the ACM
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating cost-sensitive Unsolicited Bulk Email categorization
Proceedings of the 2002 ACM symposium on Applied computing
Mining e-mail content for author identification forensics
ACM SIGMOD Record
Feature Subset Selection in Text-Learning
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Challenges of the Email Domain for Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Statistical modelling of artificial neural networks using the multi-layer perceptron
Statistics and Computing
Email classification with co-training
CASCON '01 Proceedings of the 2001 conference of the Centre for Advanced Studies on Collaborative research
Identifying Junk Electronic Mail in Microsoft Outlook with a Support Vector Machine
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
Context-Dependent Hybrid HME/HMM Speech Recognition using Polyphone Clustering Decision Trees
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Using latent semantic indexing to filter spam
Proceedings of the 2003 ACM symposium on Applied computing
Spam filters: bayes vs. chi-squared; letters vs. words
ISICT '03 Proceedings of the 1st international symposium on Information and communication technologies
Fighting the spam wars: A remailer approach with restrictive aliasing
ACM Transactions on Internet Technology (TOIT)
"In vivo" spam filtering: a challenge problem for KDD
ACM SIGKDD Explorations Newsletter
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Feature selection and feature extraction for text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
Adaptive mixtures of local experts
Neural Computation
Will New Standards Help Curb Spam?
Computer
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
E-mail abuse has been steadily increasing during the last decade. E-mail users find themselves targeted by massive quantities of unsolicited bulk e-mail, which often contains offensive language or has fraudulent intentions. Internet Service Providers (ISPs) on the other hand, have to face a considerable system overloading as the incoming mail consumes network and storage resources. Among the plethora of solutions, the most prominent in terms of cost efficiency and complexity are the text filtering approaches. Most of the approaches model the problem using linear statistical models. Despite their popularity - due both to their simplicity and relative ease of interpretation - the non-linearity assumption of data samples is inappropriate in practice. This is mainly due to the inability of other approaches to capture the apparent non-linear relationships, which characterize these samples. In this paper, we propose a margin-based feature selection approach integrated with a Hierarchical Mixtures of Experts (HME) system, which attempts to overcome limitations common to other machine-learning based approaches. By reducing the data dimensionality using effective algorithms for feature selection we evaluated our system with publicly available corpora of e-mails, characterized by very high similarity between legitimate and bulk e-mail (and thus low discriminative potential). We experimented with two different architectures, a hierarchical HME and a perceptron HME. As a result, we confirm the domination of our Spam Filtering (SF) - HME method against other machine learning approaches, which present lesser degree of recall, as well as against traditional rule-based approaches, which lack considerably in the achieved degrees of precision.