Instance-Based Learning Algorithms
Machine Learning
Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Lithology Recognition by Neural Network Ensembles
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Combining heterogeneous classifiers for word-sense disambiguation
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Comparison of four approaches to a rock facies classification problem
Computers & Geosciences
Selective fusion of heterogeneous classifiers
Intelligent Data Analysis
Sharing in teams of heterogeneous, collaborative learning agents
International Journal of Intelligent Systems
Combining Bagging, Boosting and Dagging for Classification Problems
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Neural net ensembles for lithology recognition
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Collective machine learning: team learning and classification in multi-agent systems
Collective machine learning: team learning and classification in multi-agent systems
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
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Gas and oil reservoirs have been the focus of modeling efforts for decades as an attempt to locate zones with high volumes. Certain subsurface layers and layer sequences, such as those containing shale, are known to be impermeable to gas and/or liquid. Oil and natural gas then become trapped by these layers, making it possible to drill wells to reach the supply, and extract for use. The drilling of these wells, however, is costly. In this paper, we utilize multi-agent machine learning and classifier combination to learn rock facies sequences from wireline well log data. The paper focuses on how to construct a successful set of classifiers, which periodically collaborate, to increase the classification accuracy. Utilizing multiple, heterogeneous collaborative learning agents is shown to be successful for this classification problem. Utilizing the Multi-Agent Collaborative Learning Architecture, 84.5% absolute accuracy was obtained, an improvement of about 6.5% over the best results achieved by the Kansas Geological Survey with the same data set. A number of heuristics are presented for constructing teams of multiple collaborative classifiers for predicting rock facies.