Computational geometry in C
Learning in the presence of concept drift and hidden contexts
Machine Learning
Machine Learning - Special issue on learning with probabilistic representations
Theoretical Computer Science
Early assessment of classification performance
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Bias management of bayesian network classifiers
DS'05 Proceedings of the 8th international conference on Discovery Science
Adaptive learning algorithms for Bayesian network classifiers
AI Communications
Improving the performance of an incremental algorithm driven by error margins
Intelligent Data Analysis - Knowledge Discovery from Data Streams
Adapting Bayes network structures to non-stationary domains
International Journal of Approximate Reasoning
Adaptive Bayesian network classifiers
Intelligent Data Analysis
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
Resource aware distributed knowledge discovery
Ubiquitous knowledge discovery
Resource aware distributed knowledge discovery
Ubiquitous knowledge discovery
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We introduce an adaptive prequential learning framework for Bayesian Network Classifiers which attempts to handle the cost-performance trade-off and cope with concept drift. Our strategy for incorporating new data is based on bias management and gradual adaptation. Starting with the simple Naïve Bayes, we scale up the complexity by gradually increasing the maximum number of allowable attribute dependencies, and then by searching for new dependences in the extended search space. Since updating the structure is a costly task, we use new data to primarily adapt the parameters and only if this is really necessary, do we adapt the structure. The method for handling concept drift is based on the Shewhart P-Chart. We evaluated our adaptive algorithms on artificial domains and benchmark problems and show its advantages and future applicability in real-world on-line learning systems.