University admission system using machine learning

Abstract EN

This work examines the entrance procedure for a student seeking admission to a college institute.

The system ASD (Artificial Student Decision) is a simple classifier system, which learns from the performance of the previous batches.

This experience coupled with information about his aptitude enables the expert to guide the student towards the branch best suited for him.

Genetics Based Machine Learning (GBML) forms our choice, as it is more human like, speculative, seeking better alternatives through the juxtaposition of hunches, inductive, using deductive procedures.

Apportionment of credits involved in the evaluation of aptitude is carried out using the famous Bucket Brigade Algorithm.

The tripartite process of Genetic Algorithm has been applied to make the system robust.

This work addressed an important issue in student education requirement, compares and contrasts what is involved in human learning with what is involved in machine learning.

The results shows In the long run for big knowledge based systems, learning will turn out to be more efficient than programming.

Development the LCS by using two wildcards this increase the performance of the system.