Classification Performance Enhancement for Students Realisation Model

Tarik A. Rashid

Software Engineering, College of Engineering, Salahaddin University

DOI: https://doi.org/10.17656/jzs.10416

Abstract 

    This research work aims at enhancing a classification task for student’s realisation model

at Salahadin University, Hawler, Kurdistan. 1000 records of data from different colleges

and departments at Salahadin University are collected to conduct this research work. The

collected data has been pre-processed, cleaned, filtered, normalized, then after, feature

selection techniques are applied to reduce the dimensionally of the data, finally a

classification task is carried out to find the realization of students. The results show that a

model of Support Vector Machine +Genetic Algorithm + Artificial Neural Network

produces promising results than other models.

Key Words: Forecasting Student Performance, Feature Reduction, Neural Networks, Support Vector Machines, K- Nearest Neighbors,

Genetic Algorithms.

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