Using feature selection algorithms in educational data: Three case studies

Marques, Marcelo Ladeira; Nicoletti, M. C.; Guimarães, Miguel

doi:10.23919/cisti.2017.7975749

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data pre-processing techniques like handling of missing values, noisy data and balancing the minority class instances were used before the implementation of classification technique to improve the accuracy of the classification models [12,13]. Feature selection has been used to identify the relevant attributes in student datasets and others [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A novel academic performance estimation model using two stage feature selection

Chaudhury¹,

Tripathy

2020

IJEECS

View full text Add to dashboard Cite

<span lang="EN-GB">Educational data mining has gained tremendous interest from researchers across the globe. Using data mining techniques in the field of education several significant findings have been made. Accurate academic performance estimation is a challenging task. In this study we have developed a novel model to estimate the academic performance of students. Techniques like conversion of categorical attributes into dummy variables, classification, two staged feature selection and an improved differential evolutionary algorithm were used. Our proposed model outperformed existing models of students’ academic performance determination and gave a new direction to it. The proposed model can help not only to reduce the number of academic failures but also help to comprehend the factors contributing to a student’s academic performance (poor, average or outstanding).Computer</span>

show abstract

Section: Introductionmentioning

confidence: 99%