Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Stacking Bagged and Dagged Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Email Categorization Using (2+1)-Tier Classification Algorithms
ICIS '08 Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008)
Multi-classifier Classification of Spam Email on a Ubiquitous Multi-core Architecture
NPC '08 Proceedings of the 2008 IFIP International Conference on Network and Parallel Computing
An innovative analyser for multi-classifier e-mail classification based on grey list analysis
Journal of Network and Computer Applications
Spam filtering for network traffic security on a multi-core environment
Concurrency and Computation: Practice & Experience - Multi-core Supported Network and System Security
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Multi-tier Phishing Email Classification with an Impact of Classifier Rescheduling
ISPAN '09 Proceedings of the 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Hybrid feature selection for phishing email detection
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part II
A multi-tier phishing detection and filtering approach
Journal of Network and Computer Applications
Applying clustering and ensemble clustering approaches to phishing profiling
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
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This paper is devoted to multi-tier ensemble classifiers for the detection and filtering of phishing emails. We introduce a new construction of ensemble classifiers, based on the well known and productive multi-tier approach. Our experiments evaluate their performance for the detection and filtering of phishing emails. The multi-tier constructions are well known and have been used to design effective classifiers for email classification and other applications previously. We investigate new multi-tier ensemble classifiers, where diverse ensemble methods are combined in a unified system by incorporating different ensembles at a lower tier as an integral part of another ensemble at the top tier. Our novel contribution is to investigate the possibility and effectiveness of combining diverse ensemble methods into one large multi-tier ensemble for the example of detection and filtering of phishing emails. Our study handled a few essential ensemble methods and more recent approaches incorporated into a combined multi-tier ensemble classifier. The results show that new large multi-tier ensemble classifiers achieved better performance compared with the outcomes of the base classifiers and ensemble classifiers incorporated in the multi-tier system. This demonstrates that the new method of combining diverse ensembles into one unified multi-tier ensemble can be applied to increase the performance of classifiers if diverse ensembles are incorporated in the system.