Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction

Predicting student dropout is essential for universities dealing with high attrition rates. This study compares two feature selection (FS) methods—correlation-based feature selection (CFS) and symmetrical uncertainty (SU)—in educational data mining for dropout prediction. We evaluate these methods u...

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Hlavní autoři:	Haryono Setiadi, Indah Paksi Larasati, Esti Suryani, Dewi Wisnu Wardani, Hasan Dwi Cahyono Wardani, Ardhi Wijayanto
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Ikatan Ahli Informatika Indonesia 2024-08-01
Edice:	Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Témata:	educational data mining performance evaluation correlation-based feature selection symmetrical uncertainty
On-line přístup:	https://jurnal.iaii.or.id/index.php/RESTI/article/view/5911
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