The week's pick
Reseach Article
Superiority of Rotation Forest Machine Learning Algorithm in Prediction of Students’ Performance
by
Manish Kumar
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 137 - Number 2 |
| Year of Publication: 2016 |
| Authors: Manish Kumar |
10.5120/ijca2016908712
|
Manish Kumar . Superiority of Rotation Forest Machine Learning Algorithm in Prediction of Students’ Performance. International Journal of Computer Applications. 137, 2 ( March 2016), 43-48. DOI=10.5120/ijca2016908712
@article{
10.5120/ijca2016908712,
author = {
Manish Kumar
},
title = { Superiority of Rotation Forest Machine Learning Algorithm in Prediction of Students’ Performance },
journal = {
International Journal of Computer Applications
},
issue_date = { March 2016 },
volume = { 137 },
number = { 2 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = {
43-48
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume137/number2/24251-2016908712/
},
doi = { 10.5120/ijca2016908712 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:37:18.814737+05:30
%A Manish Kumar
%T Superiority of Rotation Forest Machine Learning Algorithm in Prediction of Students’ Performance
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 2
%P 43-48
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Now-a-days the volume of educational data stored in educational database is increasing rapidly and these databases comprise hidden information for improvement of students’ performance. Therefore we need computational methods to study the data available in the educational field and bring out the hidden knowledge from it. In this study, the experiments were conducted for the prediction task of educational data obtained from UCI Machine Learning repository using the five machine learning algorithms. The feature selected is used for training and testing of each classifier individually with ten-fold cross validation. The results obtained show that the ROF classifier outperforms other classifiers in terms of Area under the ROC curve (AUC), accuracy and MCC respectively.
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Index Terms
Computer Science
Information Sciences
Keywords