Dimensions
DimensionsKit, J.L.O.W. and Asirvadam, V.S. and Hassan, M.F. (2021) Enhanced Ensemble Models for Predictive Modeling: A Conceptual Framework. In: UNSPECIFIED.
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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....
Abstract
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.
| Item Type: | Conference or Workshop Item (UNSPECIFIED) |
|---|---|
| Additional Information: | 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 |
| Uncontrolled Keywords: | Learning algorithms; Signal processing, Conceptual frameworks; Ensemble modeling; Ensemble models; Experimental approaches; Multiple learning algorithms; Prediction accuracy; Predictive modeling, Predictive analytics |
| Depositing User: | Mr Ahmad Suhairi UTP |
| Date Deposited: | 10 Nov 2023 03:29 |
| Last Modified: | 10 Nov 2023 03:29 |
| URI: | https://khub.utp.edu.my/scholars/id/eprint/15108 |
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