Binary Classification for Hydraulic Fracturing Operations in Oil & GasWells via Tree Based Logistic RBF Networks
Keywords:Hydraulic Fracturing, Radial Basis Function Neural Networks, Classification and Regression Trees, Logistic Regression
In this paper we develop a novel tree based radial basis function neural networks (RBF-
NNs) model incorporating logistic regression. We aim to improve the classication performanceÂ of logistic regression method by pre-processing the input data in RBF-NN frame. Although theÂ scope of our proposed method is binary classication in this paper, it is easy to generalize itÂ for multi-class classication problems. Furthermore, our model is very convenient to adapt forÂ n < pÂ classication problem that is very popular yet dicult topic in statistics. We show theÂ generalization and classication performance of our model using simulated data. We have alsoÂ applied our model on a real life data set gathered from hydraulic fracturing in oil & gas wells. TheÂ results show the high classication performance of our model that is superior to logistic regression.Â We have coded our model on R software. Logistic Regression applications were carried out usingÂ IBM SPSS Version 20.
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