Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Neural Computation
Reservoir Characterization Using Support Vector Machines
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Ensemble Neural Networks Using Interval Neutrosophic Sets and Bagging
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
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This paper presents a novel approach to the regression problem using bagging of complementary neural networks (CMTNN). A bagging technique is applied to an ensemble of pairs of feed-forward backpropagation neural networks created to predict degrees of truth and falsity values. In our approach, uncertainties in the prediction of the truth and falsity values are quantified based on the difference among all the predicted truth values and the difference among all the predicted falsity values in the ensemble, respectively. An aggregation technique based on uncertainty values is proposed. This study is realized to the problem of porosity prediction in well log data analysis. The results obtained from our approach are compared to results obtained from three existing bagging models. These three models are an ensemble of feed-forward backpropagation neural networks, an ensemble of general regression neural networks, and an ensemble of support vector machines. We found that our approach improves performance compared to those three existing models that apply a simple averaging technique based on only the truth porosity values in the ensemble.