The Strength of Weak Learnability
Machine Learning
Machine Learning
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Methods for Designing Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
An evaluation of statistical spam filtering techniques
ACM Transactions on Asian Language Information Processing (TALIP)
A Balanced Ensemble Approach to Weighting Classifiers for Text Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Information Sciences: an International Journal
Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
Classifier ensembles: Select real-world applications
Information Fusion
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
Developing an immunity to spam
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Using classifier fusion techniques for protein secondary structure prediction
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Ensemble-Based Incremental Learning Approach to Data Fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
An automatic method for construction of ensembles to time series prediction
International Journal of Hybrid Intelligent Systems
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Most email users have experienced spam problems, which have been addressed as text classification problem. In this paper, we propose a novel spam detection method which uses an ensemble of classifiers based on subsampling and dynamic weighted voting techniques. Since there is diversity in genre of emails' contents, the proposed method finds different topics in emails by using a clustering algorithm. The proposed algorithm first extracts disjoint clusters of emails, and then a classifier is trained on each cluster, and finally decisions of classifiers are combined using dynamic weighted majority techniques. In order to classify a new input sample, first it is compared with all cluster centers and its similarity to each cluster is identified; then the classifiers in the vicinity of the input sample obtain greater weights for the final decision of the ensemble. Finally, the outputs of the classifiers are combined using weighted voting with weights calculated from the similarity of the input sample with cluster centers. The experimental results show that the proposed algorithm outperforms pure SVM and the related ensemble based classifiers.