Estimating Software Cost with a Weighted Feature Selection and Support Vector Regression with Mixture of Kernels Ensemble Learning Method

Abstract

In traditional feature selection methods, there are only two possible outcomes: the feature is selected or the feature is not selected, which will lead to the loss of feature information. In this paper, considering the deficiencies of traditional methods and the requirement of software cost estimation, a weighted feature selection (WFS) method with the supervised wrapper mode is used in software cost estimation, which can effectively distinguish the influence of different features on the cost. In view of the good application effect of support vector regression (SVR), as well as a good performance of the mixture of kernels, the relationship model among the features and the software cost is established based on SVR with the mixture of kernels. In addition, considering the consistency of feature selection and the establishment of cost estimation model, a joint optimization method based on hybrid particle swarm optimization (HPSO) is adopted, which can achieve the influence analysis of features and the optimization of cost estimation model. Experiments show that the proposed ensemble learning method is effective.