The week's pick
Random Articles
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.
References
- Turban, E.; Sharda, R.; Aronson, J.; & King D. (2007). Business Intelligence, A Managerial Approach (2nd ed.). Prentice-Hall.
- B. M. Bidgoli, D. Kashy, G. Kortemeyer, & W. Punch (2003). Predicting student performance: An application of data mining methods with the educational web-based system lon-capa, in: Proceedings of ASEE/IEEE frontiers in education conference.
- Romero C, Ventura S (2007) Educational data mining: a survey from 1995 to 2005. Expert Syst Appl 33(1):135–146.
- Hsia T.,C., Shie A.,J,, & Chen L.,C. (2008) Course planning of extension education to meet market demand by using data mining techniques—an example of Chinkuo technology university in Taiwan. Expert Syst Appl 34:596–602.
- El-A. E., Abdel-A. R. (2008) Construction and analysis of educational tests using abductive machine learning. Comput Educ 51:1–16.
- C. Romero, S. Ventura, P. G. Espejo, C. Herv. (2008). Data mining algorithms to classify students, in: Educational Data Mining.
- V. Oladokun, A. Adebanjo, O. Charles-Owaba. (2008). Predicting students’ academic performance using artificial neural network: A case study of an engineering course, The Pacific Journal of Science and Technology 9 (1) 72–79.
- Lykourentzou I, Giannoukos I, Nikolopoulos V, Mpardis G, Loumos V (2009) Dropout prediction in e-learning courses through the combination of machine learning techniques. Comput and Educ 53:950–965.
- Jantan H , Hamdan AR, Othman ZA (2010) Classification and prediction of academic talent using data mining techniques, KES 2010, Part I, LNAI 6276, pp 491–500.
- M. Wook, Y. H. Yahaya, N. Wahab, M. R. M. Isa, N. F. Awang, H. Y. Seong. (2009) Predicting ndum student’s academic performance using data mining techniques, in: Computer and Electrical Engineering, ICCEE’09. Second International Conference on, Vol. 2, IEEE, 2009, pp. 357–361.
- M. M. Quadri, N. Kalyankar. (2010) Drop out feature of student data for academic performance using decision tree techniques, Global Journal of Computer Science and Technology 10 (2).
- S. Sembiring, M. Zarlis, D. Hartama, S. Ramliana, E. Wani. (2011). Prediction of student academic performance by an application of data mining techniques, in: International Conference on Management and Artificial Intelligence IPEDR, Vol. 6, pp. 110–114.
- M. M. A. Tair, A. M. El-Halees Mining educational data to improve students’ performance: a case study, International Journal of Information 2 (2).
- I. Hidayah, A. E. Permanasari, N. Ratwastuti.( 2013) Student classification for academic performance prediction using neuro fuzzy in a conventional classroom, in: Information Technology and Electrical Engineering (ICITEE), International Conference on, IEEE, 2013, pp. 221–225.
- P. M. Arsad, N. Buniyamin, J.-l. A. Manan (2013) A neural network students’ performance prediction model (nnsppm), in: Smart Instrumentation, Measurement and Applications (ICSIMA), 2013 IEEE International Conference on, IEEE, pp. 1–5.
- D. M. D. Angeline. (2013) Association rule generation for student performance analysis using Apriori algorithm, The SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 1 (1) p12–16.
- C. Romero, M.-I. L opez, J.-M. Luna, S. Ventura (2013). Predicting students’ final performance from participation in on-line discussion forums, Computers & Education 68 458–472.
- T. M. Christian, M. Ayub. (2014) Exploration of classification using nbtree for predicting students’ performance, in: Data and Software Engineering (ICODSE), 2014 International Conference on, IEEE, pp. 1–6.
- M. Mayilvaganan, D. Kalpanadevi. (2014) Comparison of classification techniques for predicting the performance of students’ academic environment, in: Communication and Network Technologies (ICCNT), International Conference on, IEEE, pp. 113–118.
- G. Elakia, N. J. Aarthi, (2014). Application of data mining in educational database for predicting behavioural patterns of the students, Elakia et al,/(IJCSIT) International Journal of Computer Science and Information Technologies 5 (3) 4649–4652.
- G. Gray, C. McGuinness, P. Owende. (2014) An application of classification models to predict learner progression in tertiary education, in: Advance Computing Conference (IACC), 2014 IEEE International, IEEE, 2014, pp. 549–5154.
- Raheela A., Agathe M., & Mahmood K. P. (2015). Predicting Student Academic Performance at Degree Level: A Case Study. I.J. Intelligent Systems and Applications, 01, 49-61
- Glyn, H., & Chelsea, D. (2015). The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs), Research and Practice in Technology Enhanced Learning.
- Juan, J., Rodriguez, L., & Kuncheva I. (2006). Rotation forest: a new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10), 1619–30.
- Ludmila I., Kuncheva, Jaun J., Rodriguez, (2007). An experimental study on rotation forest ensembles. Proceedings of the 7th International Conference on Multiple Classifier Systems, Springer-Verlag, Prague, Czech Republic, 459–68.
- .Witten I., H., & Ian, H., (2011). Data mining: practical machine learning tools and techniques. Morgan Kaufmann Series in Data Management Systems.
- Tom Fawcett, (2003). ROC graphs: Notes and practical considerations for data mining researchers. Technical report, HP Laboratories
Index Terms
Computer Science
Information Sciences
Keywords