Computational Hybrids Towards Software Defect Prediction s

In this paper, new computational intelligence sequential hybrid architectures involving Genetic Programming (GP) and Group Method of Data Handling (GMDH) viz. Polynomial Regression, Support Vector Regression (SVR), Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), Multilayer FeedForward Neural Network (MPFF), Radial Basis Function Neural Network (RBF), Counter Propagation Neural Network (CPNN), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), Tree Net, Group Method of Data Handling (GMDH) and Genetic Programming (GP). GP-GMDH. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are also developed. We also performed GP based feature selection. The efficacy of Multiple Linear Regression (MLR), Polynomial Regression, Support Vector Regression (SVR), Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), Multilayer FeedForward Neural Network (MLFF), Radial Basis Function Neural Network (RBF), Counter Propagation Neural Network (CPNN), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), TreeNet, Group Method of Data Handling and Genetic Programming is tested on the NASA dataset. Ten-fold cross validation and t-test are performed to see if the performances of the hybrids developed are statistically significant.