S. Fattahi

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An attractive research in recent years is solving class imbalance problem in imbalanced dataset. The class is imbalanced when the number of one class (majority) is more than another one (minority). The classification of this imbalanced class causes imbalanced distribution and poor predictive classification accuracy. This paper introduces a new ensemble(More)
This paper presents a new ensemble classifier for class imbalance problem with the emphasis on two -class (binary) classification. This novel method is a combination of SMOTE (Synthetic Minority Over-sampling Technique), Rotation Forest, and AdaBoostM1 algorithms. SMOTE was employed for the over-sampling of the minority samples at 100%, 200%, 300%, 400%,(More)
This paper presents an Artificial Neural Network (ANN) algorithm to improve oil production forecasting. ANN algorithm is developed by different data preprocessing methods and considering different training algorithms and transfer functions in ANN models. Bayesian regularization backpropagation (BR), Levenberg-Marquardt back propagation (LM) and Gradient(More)
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