Original Contribution: Stacked generalization
Neural Networks
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Classification for Imprecise Environments
Machine Learning
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Evolving Receiver Operating Characteristics for Data Fusion
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
A Sense of Self for Unix Processes
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Pareto analysis for the selection of classifier ensembles
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Beam sampling for the infinite hidden Markov model
Proceedings of the 25th international conference on Machine learning
Threshold-optimized decision-level fusion and its application to biometrics
Pattern Recognition
Repairing concavities in ROC curves
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A new ensemble diversity measure applied to thinning ensembles
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Combining hidden Markov models for improved anomaly detection
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
IEEE Transactions on Information Theory
A probabilistic model of classifier competence for dynamic ensemble selection
Pattern Recognition
Adaptive ROC-based ensembles of HMMs applied to anomaly detection
Pattern Recognition
Incremental Boolean combination of classifiers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
An online AUC formulation for binary classification
Pattern Recognition
A survey of techniques for incremental learning of HMM parameters
Information Sciences: an International Journal
Fusion of biometric systems using Boolean combination: an application to iris-based authentication
International Journal of Biometrics
Multi-objective evolutionary optimization for generating ensembles of classifiers in the ROC space
Proceedings of the 14th annual conference on Genetic and evolutionary computation
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
A Multi-Classifier System for Sentiment Analysis and Opinion Mining
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Pattern Recognition
A classifier fusion system for bearing fault diagnosis
Expert Systems with Applications: An International Journal
Cost-sensitive decision tree ensembles for effective imbalanced classification
Applied Soft Computing
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Hidden Markov models (HMMs) have been shown to provide a high level performance for detecting anomalies in sequences of system calls to the operating system kernel. Using Boolean conjunction and disjunction functions to combine the responses of multiple HMMs in the ROC space may significantly improve performance over a ''single best'' HMM. However, these techniques assume that the classifiers are conditional independent, and their of ROC curves are convex. These assumptions are violated in most real-world applications, especially when classifiers are designed using limited and imbalanced training data. In this paper, the iterative Boolean combination (IBC) technique is proposed for efficient fusion of the responses from multiple classifiers in the ROC space. It applies all Boolean functions to combine the ROC curves corresponding to multiple classifiers, requires no prior assumptions, and its time complexity is linear with the number of classifiers. The results of computer simulations conducted on both synthetic and real-world host-based intrusion detection data indicate that the IBC of responses from multiple HMMs can achieve a significantly higher level of performance than the Boolean conjunction and disjunction combinations, especially when training data are limited and imbalanced. The proposed IBC is general in that it can be employed to combine diverse responses of any crisp or soft one- or two-class classifiers, and for wide range of application domains.