An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things

An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things

We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based, wrapper-based and embedded feature selection methods, our architecture enables the automatic selectio...

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Bibliographic Details
Main Authors: Mert Nakip, Kubilay Karakayali, Cuneyt Guzelis, Volkan Rodoplu
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Forecasting
feature selection
machine learning
neural network
Internet of Things (IoT)
predictive network
Online Access:https://ieeexplore.ieee.org/document/9477183/

Internet

https://ieeexplore.ieee.org/document/9477183/

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