Volume 18, No. 6, 2021
A Hybrid Outlier Detection-Based Dropout Prediction Model On Student Databases
Anil Kumar Tiwari , Sanjay Kumar
Abstract
Machine learning tools and techniques play a vital role in the education field and real-time applications. Most of the traditional machine learning models uses static metrics, limited data size and limited feature space due to high computational processing time. In this work, a hybrid outlier detection and data transformation approaches are implemented on the anomaly databases. In this work, hybrid outlier detection and data transformation approaches are implemented on the anomaly databases. Proposed data filtering module is applicable to high dimensional data size and feature space for classification problem. In the classification problem, an advanced boosting classifier is implemented on the filtered data in order to improve the true positive and error rate. Experimental results are simulated on MOOC dropout datasets with different feature space size and data size. Simulation results proved that the proposed boosting classifier has better error rate and statistical accuracy than the conventional approaches.
Pages: 8642-8663
Keywords: Student dropout database; Machine Learning, Support vector machine.