On-Line Estimation of Hidden Markov Model Parameters
DS '00 Proceedings of the Third International Conference on Discovery Science
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A comparison of techniques for on-line incremental learning of HMM parameters in anomaly detection
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
An adaptive classification system for video-based face recognition
Information Sciences: an International Journal
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Determining the operational limits of an anomaly-based intrusion detector
IEEE Journal on Selected Areas in Communications
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)
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The incremental Boolean combination (incrBC) technique is a new learn-and-combine approach that is proposed to adapt ensemble-based pattern classification systems over time, in response to new data acquired during operations. When a new block of training data becomes available, this technique generates a diversified pool of base classifiers from the data by varying training hyperparameters and random initializations. The responses of these classifiers are then combined with those of previously-trained classifiers through Boolean combination in the ROC space. Through this process, an ensemble is selected from the pool, where Boolean fusion functions and thresholds are adapted for improved accuracy, while redundant base classifiers are pruned. Results of computer simulations conducted using Hidden Markov Models (HMMs) on synthetic and real-world host-based intrusion detection data indicate that incrBC can sustain a significantly higher level of accuracy than when the parameters of a single best HMM are re-estimated for each new block of data, using reference batch and incremental learning techniques. It also outperforms static fusion techniques such as majority voting for combining the responses of new and previously-generated pools of HMMs. Pruning prevents pool sizes from increasing indefinitely over time, without adversely affecting the overall ensemble performance.