TY  - JOUR
AV  - none
TI  - A predictive model for student outcomes using sparse coding â?? Hybrid features selection
SP  - 7124
N1  - cited By 0
SN  - 19928645
PB  - Little Lion Scientific
EP  - 7138
ID  - scholars9676
N2  - 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 â??Features Selectionâ?? 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 â?? ongoing JATIT & LLS.
IS  - 21
Y1  - 2018///
VL  - 96
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057162764&partnerID=40&md5=48962e173aefef530c3aa6e22e68a885
A1  - Zaffar, M.
A1  - Hashmani, M.A.
A1  - Savita, K.S.
A1  - Qayyum, A.
JF  - Journal of Theoretical and Applied Information Technology
ER  -