TY  - CONF
N2  - Student's academic performance is the main focus of all educational institutions. Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. The obtained results of the different FS algorithms and classifiers will also help the new researchers in finding the best combinations of FS algorithms and classifiers. Selecting relevant features for student prediction model is very sensitive issue for educational stakeholders, as they have to take decisions on the basis of results of prediction models. Furthermore our paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention. © 2017 IEEE.
N1  - cited By 41; Conference of 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017 ; Conference Date: 16 November 2017 Through 17 November 2017; Conference Code:134594
TI  - Performance analysis of feature selection algorithm for educational data mining
ID  - scholars8533
SP  - 7
KW  - Classification (of information); Data mining; Education computing; Feature Selection
KW  -  Academic performance; Data-mining techniques; Educational data mining; Educational institutions; Feature selection algorithm; Performance of classifier; Performances analysis; Prediction modelling; Research areas
KW  -  Students
AV  - none
A1  - Zaffar, M.
A1  - Hashmani, M.A.
A1  - Savita, K.S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047422879&doi=10.1109%2fICBDAA.2017.8284099&partnerID=40&md5=21c800fbabd5cf67efbddf636376cc02
EP  - 12
VL  - 2018-J
Y1  - 2017///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781538607909
ER  -