eprintid: 9676
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/96/76
datestamp: 2023-11-09 16:36:19
lastmod: 2023-11-09 16:36:19
status_changed: 2023-11-09 16:29:33
type: article
metadata_visibility: show
creators_name: Zaffar, M.
creators_name: Hashmani, M.A.
creators_name: Savita, K.S.
creators_name: Qayyum, A.
title: A predictive model for student outcomes using sparse coding � Hybrid features selection
ispublished: pub
note: cited By 0
abstract: Educational data mining is a new research area and is used to predict student performance and provides insight that allows educators to plan accordingly. Its results now play an important role in improving educational standards. Specific algorithms for �Features Selection� optimize the classification accuracy of a prediction model. This work introduces a new method based on sparse representation for features selection and reduction that assesses predictive model's accuracy, precision and recall. Different existing features selection methods are fused and passed to a classifier to measure performance using educational datasets. Experimental results are compared to existent features selection techniques and demonstrate that the proposed approach provides superior solution for data fusion and individual (single) predictive outcomes. © 2005 � ongoing JATIT & LLS.
date: 2018
publisher: Little Lion Scientific
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057162764&partnerID=40&md5=48962e173aefef530c3aa6e22e68a885
full_text_status: none
publication: Journal of Theoretical and Applied Information Technology
volume: 96
number: 21
pagerange: 7124-7138
refereed: TRUE
issn: 19928645
citation:   Zaffar, M. and Hashmani, M.A. and Savita, K.S. and Qayyum, A.  (2018) A predictive model for student outcomes using sparse coding � Hybrid features selection.  Journal of Theoretical and Applied Information Technology, 96 (21).  pp. 7124-7138.  ISSN 19928645