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Semi-Supervised Classification in Educational Data Mining: Students’ Performance Case Study
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 179 - Number 26 |
| Year of Publication: 2018 |
| Authors: Nur Uylaş Satı |
10.5120/ijca2018916549
|
Nur Uylaş Satı . Semi-Supervised Classification in Educational Data Mining: Students’ Performance Case Study. International Journal of Computer Applications. 179, 26 ( Mar 2018), 13-17. DOI=10.5120/ijca2018916549
Abstract
Semi-supervised learning is one of the significant field in machine learning or data mining. It deals with datasets that have many unlabeled and a few labeled samples. In this study we aim to predict students’ success in educational institutes by use of semi-supervised classification methods in the well- known machine learning tool WEKA. The methods are explained in detail and for every method, implementations are done on a special dataset called “Students’ performance”. The effectiveness of the methods are tried to be increased by using attrbibute selection functions in data selection and transformation processes. The performance of the algorithms are stated by accuracy results presented in tables and figures.
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