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Theses

Adaptive machine learning algorithms for data streams subject to concept drifts

Abstract : In this thesis, we investigate the problem of supervised classification on a data stream subject to concept drifts. In order to learn in this environment, we claim that a successful learning algorithm must combine several characteristics. It must be able to learn and adapt continuously, it shouldn’t make any assumption on the nature of the concept or the expected type of drifts and it should be allowed to abstain from prediction when necessary. On-line learning algorithms are the obvious choice to handle data streams. Indeed, their update mechanism allows them to continuously update their learned model by always making use of the latest data. The instance based (IB) structure also has some properties which make it extremely well suited to handle the issue of data streams with drifting concepts. Indeed, IB algorithms make very little assumptions about the nature of the concept they are trying to learn. This grants them a great flexibility which make them likely to be able to learn from a wide range of concepts. Another strength is that storing some of the past observations into memory can bring valuable meta-informations which can be used by an algorithm. Furthermore, the IB structure allows the adaptation process to rely on hard evidences of obsolescence and, by doing so, adaptation to concept changes can happen without the need to explicitly detect the drifts. Finally, in this thesis we stress the importance of allowing the learning algorithm to abstain from prediction in this framework. This is because the drifts can generate a lot of uncertainties and at times, an algorithm might lack the necessary information to accurately predict.
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https://tel.archives-ouvertes.fr/tel-01812044
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Submitted on : Monday, December 3, 2018 - 2:42:07 PM
Last modification on : Sunday, October 25, 2020 - 7:32:13 PM
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2017PA066496.pdf
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  • HAL Id : tel-01812044, version 2

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Pierre-Xavier Loeffel. Adaptive machine learning algorithms for data streams subject to concept drifts. Machine Learning [cs.LG]. Université Pierre et Marie Curie - Paris VI, 2017. English. ⟨NNT : 2017PA066496⟩. ⟨tel-01812044v2⟩

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