Prediction Algorithm for Software Defect Series Based on Nonlinear Weighted Ensemble Learning

Aiming at the problem of the basic prediction algorithm with relative low prediction accuracy, a novel and improved prediction algorithm NLWEPrediction is proposed based on non-linear weighted and ensemble learning. It combines the advantages of linear ensemble learning and the relationship between the base predict algorithms, which corrects the prediction deviation and uses gradient descent method to calculate the model parameters. The experiments proved that the NLWEPrediction's mean squared error in datasets is lower than 250, and the mean absolute difference is lower than 13. The algorithm was compared with its four base prediction algorithms, other two ensemble prediction algorithms Bagging and Stacking and original NLWEPrediction for efficiency analysis. Experimental results showed that NLWEPrediction has obviously low mean square error and average absolute error. The prediction accuracy is improved. So, adding the nonlinear regression terms can improve the capability of ensemble classifier.