Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
C4.5: programs for machine learning
C4.5: programs for machine learning
A hierarchical classifier system implementing a motivationally autonomous animat
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
A new version of the rule induction system LERS
Fundamenta Informaticae
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning and Data Mining; Methods and Applications
Machine Learning and Data Mining; Methods and Applications
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
Machine Learning
Machine Learning
Triggered Rule Discovery in Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
A Reinforcement Learning Based Neural Multi-Agent-System for Control of a Combustion Process
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Classifier fitness based on accuracy
Evolutionary Computation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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A data mining approach was applied to analyze relationships among 54 parameters of a circulating fluidized-bed boiler. Knowledge was extracted from the data by machine learning algorithms. The extracted knowledge was used to determine ranges of process parameters (control signatures) that led to the increased efficiency of the combustion process. The research has shown that the efficiency can be predicted to the same degree of accuracy with and without the data describing the fuel composition or boiler demand levels. This discovery might have profound impact on the research directions in optimization of the energy production. Adjusting parameters of the control system has led to improved efficiency of the combustion process. The proposed data mining approach is applicable to different types of burners and fuel types. It is well suited to perform tradeoff analysis between various performable measures, e.g., efficiency and emissions.