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http://hdl.handle.net/10609/149186
Title: | Cascading and Ensemble Techniques in Deep Learning |
Author: | de Zarzà i Cubero, I. de Curtò y DíAz, J. Hernández-Orallo, Enrique Calafate, Carlos |
Citation: | de Zarzà, I. de Curtò, J. Hernández-Orallo, E. [Enrique]. Calafate, C. [Carlos T.] (2023). Cascading and Ensemble Techniques in Deep Learning. Electronics, 12(15), 1-18. doi: 10.3390/electronics12153354 |
Abstract: | In this study, we explore the integration of cascading and ensemble techniques in Deep Learning (DL) to improve prediction accuracy on diabetes data. The primary approach involves creating multiple Neural Networks (NNs), each predicting the outcome independently, and then feeding these initial predictions into another set of NN. Our exploration starts from an initial pre- liminary study and extends to various ensemble techniques including bagging, stacking, and finally cascading. The cascading ensemble involves training a second layer of models on the predictions of the first. This cascading structure, combined with ensemble voting for the final prediction, aims to exploit the strengths of multiple models while mitigating their individual weaknesses. Our results demonstrate significant improvement in prediction accuracy, providing a compelling case for the potential utility of these techniques in healthcare applications, specifically for prediction of diabetes where we achieve compelling model accuracy of 91.5% on the test set on a particular challenging dataset, where we compare thoroughly against many other methodologies. |
Keywords: | neural networks cascading ensemble diabetes |
DOI: | https://doi.org/10.3390/electronics12153354 |
Document type: | info:eu-repo/semantics/article |
Version: | info:eu-repo/semantics/publishedVersion |
Issue Date: | 2-Aug-2023 |
Publication license: | http://creativecommons.org/licenses/by/4.0/es/ |
Appears in Collections: | Articles cientÍfics Articles |
Files in This Item:
File | Description | Size | Format | |
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Cascading_Zarzà_ECT_MDPI.pdf | 513,2 kB | Adobe PDF | View/Open |
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