A decision support system approach for accreditation & quality assurance council at higher education institutions in Yemen

Classification is used for discovery of a predictive learning function that classifies data item into one of several predefined classes. e.g., classify universities based on students number or based on offered programs, or classify cars based on gas mileage, and Presentation it by decision-tree, classification rule, neural network, and genetic algorithms, etc. It is noted that, there are large amount of data obtained from the universities. We need to evaluate the accurate assessment of the performance of any institution. Currently the decision in ministry of higher education and scientific research is taken randomly, not based on logical analysis. Moreover, the education towards to universal, the ministry of higher education in Yemen has to activate the council of accreditation, and support it to start quickly and effectively. In this paper, we have used approach to assist the council for accreditation to start automation for accreditation operations and mechanisms, that's proposed by using machine learning techniques, also to help decision makers for taking accurate and swift decisions. Our study is used to classify institution that wants to take a license from council to three classes: grant license of accreditation, grand license provided to improvement has done, or not granting accreditation license. We have used the intelligent algorithms for calculate probability of grand accreditation license based on degree of council standards, which we used to predict through a model building to classification by using Naïve Bayes algorithm. The proposed method is typically for evaluation the new institution by depends on evaluation of existing institutions. We experiment the proposed framework by flexible parameters and attributes with private training dataset, that's carefully generated and tested using real life applications. In addition, we implemented our proposed approach as a program.