Modern Applied Science; Vol. 8, No. 6; 2014 ISSN 1913-1844 E-ISSN 1913-1852 Published by Canadian Center of Science and Education 229 Estimation of Project Completion Time-Based on a Mixture of Expert in an Interactive Space M. T. Hajiali 1 , M. R. Mosavi 2 & K. Shahanaghi 3 1 Ph.D. Student, Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran 2 Professor, Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran 3 Assistant Professor, Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran Correspondence: M. T. Hajiali, Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran 16846-13114, Iran. E-mail: hajialinajar@iust.ac.ir Received: September 4, 2014 Accepted: October 8, 2014 Online Published: November 8, 2014 doi:10.5539/mas.v8n6p229 URL: http://dx.doi.org/10.5539/mas.v8n6p229 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. Keywords: Principal Component Analysis (PCA), system reliability control, project management, decision making 1. Introduction One of the most effective methods in predicting the time and the cost of project completion is use of earned value management. Formula-based methods were used frequently in the past, but nowadays, with increasing volume of received information, from projects, an increasing need to more powerful and more reliable methods, is felt. The methods based on data mining, gradually have widely been used as one of the important methods in this field. Among these methods, can refer to the methods based on neural network, which are used in [1]; in this paper, a neural network with 5 inputs and 5 outputs, and also a hidden layer, trained to predict the actual cost. The process for the minimization of the error is repeated, step by step, until a termination criterion for the system is achieved. Methods based on time series, also have been able to provide, acceptable results in this field, especially, high ability of these methods in predicting future events is the most important factor of these methods [2, 3].Methods based on S shape curves are the other methods, which be extensively used in this field [4, 5, 6].In paper [6], using S shape curves, and the relationship between cash flow and project progress; linear and nonlinear models of project progress, taking into consideration uncertainties in cash flow. In paper [7], a dynamic model obtained from earn value management (EVM), using ARMA from the project progress, and then, in an uncertainty space, time and cost of completion of the work, estimated using Kalman fuzzy method. Support Vector Machine (SVM) is type of learning machines, which has a very high ability in modeling datasets, with a highly complex feature space. Hence, several methods based on SVM have been proposed for estimating the time and cost of projects, which proposed in[8, 9, 10]. In paper [8], a combination of genetic algorithm and