Estimation of project completion time-based on a mixture of expert in an interactive space

Abstract

Estimation the time and the cost of completing projects on the basis of decision making to use either of the estimation methods are one of the most important issues in project management. In this paper, a decision making database of learning machines, is proposed, that a set of possible estimator are working together to estimate the project completion time, in it. This cooperation is based on samples neighborhood in the feature space. One of the important issues, that learning machines are facing it, is the complexity in feature space, because of features with high-correlation. In this paper, to avoid this problem, principal component analysis (PCA) method is used to accuracy has increase, addition to, increasing in system speed. Moreover, methods based on the ensemble, have a higher reliability and ability to generalization, compared to single methods. Furthermore, the hybrid method, (PCA and ensemble), have all the above mentioned advantages. Therefore, system reliability control, using more powerful learning machines, in ensemble, and also ability of the proposed model, to manage existing poor estimators, in ensemble, are other important features of this method. In the end, a software code was created, which provides ability to connect to MSP.