Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Applied multivariate statistical analysis
Applied multivariate statistical analysis
Neurocomputing
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
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lithology Recognition by Neural Network Ensembles
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial 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
Irregularity detection on low tension electric installations by neural network ensembles
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Collaborative multi-agent rock facies classification from wireline well log data
Engineering Applications of Artificial Intelligence
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Lithology recognition is a common task found in the petroleum exploration field. Roughly speaking, it is a problem of classifying rock types, based on core samples obtained from well drilling programs. In this paper we evaluate the performance of different ensemble systems, specially developed for the task of lithology recognition, based on well data from a major petroleum company. Among the procedures for creating committee members we applied Driven Pattern Replication (DPR), Bootstrap and ARC-X4 techniques. With respect to the available combining methods, Averaging, Plurality Voting, Borda Count and Fuzzy Integrals were selected. The paper presents results obtained with ensembles derived from these different methods, evaluating their performance against the single neural network classifier. The results confirm the effectiveness of applying ensembles in real world classification problems.