Concerning that traditional single classifiers have poor classification effect for imbalanced data classification, a new binary-class imbalanced data classification algorithm was proposed based on Generative Adversarial Nets (GAN) and ensemble learning, namely Generative Adversarial Nets-Adaptive Boosting-Decision Tree (GAN-AdaBoost-DT). Firstly, GAN training was adopted to get a generative model which produced minority class samples to reduce imbalance ratio. Then, the minority class samples were brought into Adaptive Boosting (AdaBoost) learning framework and their weights were changed to improve AdaBoost model and classification performance of AdaBoost with Decision Tree (DT) as base classifier. Area Under the Carve (AUC) was used to evaluate the performance of classifier when dealing with imbalanced classification problems. The experimental results on credit card fraud data set illustrate that compared with synthetic minority over-sampling ensemble learning method, the accuracy of the proposed algorithm was increased by 4.5%, the AUC of it was improved by 6.5%; compared with modified synthetic minority over-sampling ensemble learning method, the accuracy was increased by 4.9%, the AUC was improved by 5.9%; compared with random under-sampling ensemble learning method, the accuracy was increased by 4.5%, the AUC was improved by 5.4%. The experimental results on other data sets of UCI and KEEL illustrate that the proposed algorithm can improve the accuracy of imbalanced classification and the overall classifier performance.

With the arrival of big data, network public opinion data reveals the features of massive information and wide coverage. For the complicated network public opinion data, traditional single models may not efficiently predict the trend of network public opinion. To address this question, the improved combination model based on the Empirical Mode Decomposition-AutoRegression (EMD-AR) model was proposed, called EMD-ARXG (Empirical Mode Decomposition-AutoRegression based on eXtreme Gradient boosting)model. EMD-ARXG model was applied to the prediction of the trend of complex network public opinion. In this model, the Empirical Mode Decomposition (EMD) algorithm was employed to decompose the time series, and then AutoRegression (AR) model was applied to fit the decomposed time series and establish sub-models. Finally, the sub-models were reconstructed and then the modelling process was completed. In addition, in the fitting process AR model, in order to reduce the fitting error, the residual error was learned by eXtreme Gradient Boosting (XGBoost), and each sub-model was iteratively updated to improve its prediction accuracy. In order to verify the prediction performance of EMD-ARXG model, the proposed model was compared with wavelet neural network model and back propagation neural network based on EMD model. The experimental results show that the EMD-ARXG model is superior to two other models in terms of the statistical indicators including Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Theil Inequality Coefficient (TIC).