Enhancement of student performance prediction using modified K-nearest neighbor
| Main Authors: | Taha Ahmed, Saja; Iraqi Commission for Computers & Informatics (IIPS-ICCI), Al-Hamdani, Rafah; Iraqi Commission for Computers & Informatics (IIPS-ICCI), Sadik Croock, Muayad; University of Technology Baghdad |
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| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Ahmad Dahlan
, 2020
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| Subjects: | |
| Online Access: |
http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/13849 http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/13849/8295 |
Daftar Isi:
- The traditional K-nearest neighbor (KNN) algorithm uses an exhaustive search for a complete training set to predict a single test sample. This procedure can slow down the system to consume more time for huge datasets. The selection of classes for a new sample depends on a simple majority voting system that does not reflect the various significance of different samples (i.e. ignoring the similarities among samples). It also leads to a misclassification problem due to the occurrence of a double majority class. In reference to the above-mentioned issues, this work adopts a combination of moment descriptor and KNN to optimize the sample selection. This is done based on the fact that classifying the training samples before the searching actually takes place can speed up and improve the predictive performance of the nearest neighbor. The proposed method can be called as fast KNN (FKNN). The experimental results show that the proposed FKNN method decreases original KNN consuming time within a range of (75.4%) to (90.25%), and improve the classification accuracy percentage in the range from (20%) to (36.3%) utilizing three types of student datasets to predict whether the student can pass or fail the exam automatically.