Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Log Analysis of Subsurface Geology: Concepts and Computer Methods
Log Analysis of Subsurface Geology: Concepts and Computer Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Neural net ensembles for lithology recognition
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Collaborative multi-agent rock facies classification from wireline well log data
Engineering Applications of Artificial Intelligence
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This paper investigates the advantages of methods based on Neural Network Classifier Ensembles - sets of neural networks working in a cooperative way to achieve a consensus decision- in the solution of the lithology recognition problem, a common task found in the petroleum exploration field. Classifier ensembles (Committees) are developed here in two stages: first, by applying procedures for creating complementary networks, i.e., networks that are individually accurate but cause distinct misclassifications; second, by applying a combining method to those networks outputs. Among the procedures for creating committee members, the Driven Pattern Replication (DPR) was chosen for the experiments, along with the ARC-X4 technique. With respect to the available combining methods, Averaging and Fuzzy Integrals were selected. All these choices were based on previous work in the field. This paper proves the effectiveness of applying ensembles in the recognition of geological facies and suggests algorithms that might be successfully applied to others classification problems.