TY  - CONF
SN  - 9780738143972
TI  - Enhanced Ensemble Models for Predictive Modeling: A Conceptual Framework
ID  - scholars15108
A1  - Kit, J.L.O.W.
A1  - Asirvadam, V.S.
A1  - Hassan, M.F.
EP  - 28
Y1  - 2021///
KW  - Learning algorithms; Signal processing
KW  -  Conceptual frameworks; Ensemble modeling; Ensemble models; Experimental approaches; Multiple learning algorithms; Prediction accuracy; Predictive modeling
KW  -  Predictive analytics
N1  - cited By 1; Conference of 17th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2021 ; Conference Date: 5 March 2021 Through 6 March 2021; Conference Code:167956
SP  - 24
PB  - Institute of Electrical and Electronics Engineers Inc.
N2  - Ensemble Model learnings refers to a collection of techniques that combine multiple learning algorithms to improve overall prediction accuracy and persistency. This paper looks into the conceptual framework of how to enhance the ensemble model techniques and provide a comprehensive study on predictive model approach using an enhanced ensemble model. We explore the framework, performance evaluation and experimental approach. © 2021 IEEE.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103689507&doi=10.1109%2fCSPA52141.2021.9377299&partnerID=40&md5=4717f7d3d5959cf03071b90427cf22c8
AV  - none
ER  -