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Machine learning algorithms for dynamic Internet of Things

Abstract : With the rapid growth of Internet-of-Things (IoT) devices and sensors, sources that are continuously releasing and curating vast amount of data at high pace in the form of stream. The ubiquitous data streams are essential for data driven decisionmaking in different business sectors using Artificial Intelligence (AI) and Machine Learning (ML) techniques in order to extract valuable knowledge and turn it to appropriate actions. Besides, the data being collected is often associated with a temporal indicator, referred to as temporal data stream that is a potentially infinite sequence of observations captured over time at regular intervals, but not necessarily. Forecasting is a challenging tasks in the field of AI and aims at understanding the process generating the observations over time based on past data in order to accurately predict future behavior. Stream Learning is the emerging research field which focuses on learning from infinite and evolving data streams. The thesis tackles dynamic model combination that achieves competitive results despite their high computational costs in terms of memory and time. We study several approaches to estimate the predictive performance of individual forecasting models according to the data and contribute by introducing novel windowing and meta-learning based methods to cope with evolving data streams. Subsequently, we propose different selection methods that aim at constituting a committee of accurate and diverse models. The predictions of these models are then weighted and aggregated. The second part addresses model compression that aims at building a single model to mimic the behavior of a highly performing and complex ensemble while reducing its complexity. Finally, we present the first streaming competition ”Real-time Machine Learning Competition on Data Streams”, at the IEEE Big Data 2019 conference, using the new SCALAR platform
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Submitted on : Monday, December 27, 2021 - 4:20:07 PM
Last modification on : Tuesday, December 28, 2021 - 3:04:12 AM
Long-term archiving on: : Monday, March 28, 2022 - 6:27:35 PM


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  • HAL Id : tel-03503316, version 1



Dihia Boulegane. Machine learning algorithms for dynamic Internet of Things. Machine Learning [cs.LG]. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAT048⟩. ⟨tel-03503316⟩



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