Graduate School Application Advisor Based on Neural Classification System
Neural classification systems are widely used in many fields for making logical decisions. This paper envisages a neural classification system based on back propagation algorithm to suggest an advisory model for graduate school admissions. It uses real and synthetically generated data to advise the students about the group of graduate schools where they have the maximum probability of getting selected. The system takes into consideration all the important aspects of the student’s application such as: the GPA, GRE score, number of publications, professor recommendation, parent institute rating and work experience in order as to suggest the group of potential schools. A new parameter named Student Rating Index (SRI) is also defined for a better representation of the quality of professor recommendation. The system comprises of a two-layer feed-forward network, with sigmoid hidden and output neurons to classify the data sets. The results are verified using mean square error method, Receiver Operator Characteristic (ROC) curve and confusion matrices. The verification confirms that the proposed system is an accurate and reliable representation. Thus the proposed advisory system can be used by the students to make more focused applications in the graduate schools.
KeywordsNeural classification Graduate school application Advisory model Back propagation algorithm
The authors would like to thank Mr. Pushpal Mazumder of the Department of Civil Engineering at the Indian Institute of Technology Kharagpur for his help in the data extraction process.
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