Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian method for constructing Bayesian belief networks from databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Proactive Network Fault Detection
INFOCOM '97 Proceedings of the INFOCOM '97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution
Proceedings of the 2006 ACM symposium on Applied computing
Design and Analysis of Experiments
Design and Analysis of Experiments
Learning dynamic algorithm portfolios
Annals of Mathematics and Artificial Intelligence
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Automatic parameter tuning with a Bayesian case-based reasoning system. A case of study
Expert Systems with Applications: An International Journal
Mobile Data Mining for Intelligent Healthcare Support
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
Agent Mining: The Synergy of Agents and Data Mining
IEEE Intelligent Systems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Automatic algorithm configuration based on local search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Self-configuring data mining for ubiquitous computing
Information Sciences: an International Journal
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The growing demand of data mining services for ubiquitous computing environments necessitates deployment of appropriate mechanisms that make use of circumstantial factors to adapt the data mining behavior. Despite the efforts and results so far for efficient parameter tuning, incorporating dynamically changing context information on the parameter setting decision is lacking in the present work. Thus, Bayesian networks are used to learn, in possible situations the effects of data mining algorithm parameters on the final model obtained. Based on this knowledge, we propose to infer future algorithm configurations appropriate for situations. Instantiation of the approach for association rules is also shown in the paper and the feasibility of the approach is validated by the experimentation.