A Selective Transfer Learning Method for Concept Drift Adaptation

Xie, Ge; Sun, Yu; Lin, Minlong; Tang, Ke

doi:10.1007/978-3-319-59081-3_42

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…The community has also made strides in developing strategies for dealing with the shift once it was detected. We refer interested readers to the works on using domain adaptation [48], meta-learning [46] and transfer learning [152,157] as ways of addressing dataset shift.…”

Section: Tools and Servicesmentioning

confidence: 99%

Challenges in Deploying Machine Learning: A Survey of Case Studies

Urma²,

2022

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In recent years, machine learning has transitioned from a field of academic research interest to a field capable of solving real-world business problems. However, the deployment of machine learning models in production systems can present a number of issues and concerns. This survey reviews published reports of deploying machine learning solutions in a variety of use cases, industries and applications and extracts practical considerations corresponding to stages of the machine learning deployment workflow. By mapping found challenges to the steps of the machine learning deployment workflow we show that practitioners face issues at each stage of the deployment process. The goal of this paper is to lay out a research agenda to explore approaches addressing these challenges.

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Section: Tools and Servicesmentioning

confidence: 99%

Challenges in Deploying Machine Learning: A Survey of Case Studies

Urma²,

2022

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“…However, in real-world applications, we can easily collect auxiliary data from multiple source domains. Therefore, the studies of multi-source domains transfer learning have gradually attracted the interest of researchers [13,14,15,16,17,18,19,20,21,22,23,24,25]. It can transfer knowledge from multiple source domains to learning tasks of the target domain compared to previous transfer learning algorithms with single domains [26].…”

Section: Related Workmentioning

confidence: 99%

“…Transfer learning has achieved remarkable results in resisting this challenge by transferring knowledge from source to target domains with different distributions [13]. Therefore, transfer learning attracts more and more researcher attention and has made great progress: Gao et al [14] proposed a local weighted embedded transfer learning algorithm LWE; a feature-based space transfer learning method LMPROJ are proposed by Brain et al [15]; Lu et al [16] proposed a selective transfer algorithm STLCF for collaborative filtering; Long et al [17] proposed an SVM-based least squares transfer learning framework ARTL; Xie et al [18] applied transfer learning to incremental learning and proposed an STIL algorithm; Li et al [19] proposed a new transfer learning algorithm TL-DAKELM based on the extreme learning machine; Li et al [20] proposed a transfer learning algorithm, RankRE-TL.…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Deep Transfer Neural Network Algorithm

Xue

et al. 2019

Sensors

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Transfer learning can enhance classification performance of a target domain with insufficient training data by utilizing knowledge relating to the target domain from source domain. Nowadays, it is common to see two or more source domains available for knowledge transfer, which can improve performance of learning tasks in the target domain. However, the classification performance of the target domain decreases due to mismatching of probability distribution. Recent studies have shown that deep learning can build deep structures by extracting more effective features to resist the mismatching. In this paper, we propose a new multi-source deep transfer neural network algorithm, MultiDTNN, based on convolutional neural network and multi-source transfer learning. In MultiDTNN, joint probability distribution adaptation (JPDA) is used for reducing the mismatching between source and target domains to enhance features transferability of the source domain in deep neural networks. Then, the convolutional neural network is trained by utilizing the datasets of each source and target domain to obtain a set of classifiers. Finally, the designed selection strategy selects classifier with the smallest classification error on the target domain from the set to assemble the MultiDTNN framework. The effectiveness of the proposed MultiDTNN is verified by comparing it with other state-of-the-art deep transfer learning on three datasets.

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“…Dealing with concept drift, where the generating distribution p t−1 = p t in at least some part of the stream, is a major focus of the data-stream literature, since it means that the model current h t has become partially or fully invalid. Almost all papers on the topic propose some way to tackle its implications, e.g., [14,28,11,6,18,16,17]. A comprehensive survey to concept drift in streams is given in [11].…”

Section: Dealing With Concept Driftmentioning

confidence: 99%

Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation

Read

2018

Preprint

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Learning from data streams is an increasingly important topic in data mining, machine learning, and artificial intelligence in general. A major focus in the data stream literature is on designing methods that can deal with concept drift, a challenge where the generating distribution changes over time. A general assumption in most of this literature is that instances are independently distributed in the stream. In this work we show that, in the context of concept drift, this assumption is contradictory, and that the presence of concept drift necessarily implies temporal dependence; and thus some form of time series. This has important implications on model design and deployment. We explore and highlight the these implications, and show that Hoeffding-tree based ensembles, which are very popular for learning in streams, are not naturally suited to learning within drift; and can perform in this scenario only at significant computational cost of destructive adaptation. On the other hand, we develop and parameterize gradientdescent methods and demonstrate how they can perform continuous adaptation with no explicit driftdetection mechanism, offering major advantages in terms of accuracy and efficiency. As a consequence of our theoretical discussion and empirical observations, we outline a number of recommendations for deploying methods in concept-drifting streams.

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A Selective Transfer Learning Method for Concept Drift Adaptation

Cited by 8 publications

References 14 publications

Challenges in Deploying Machine Learning: A Survey of Case Studies

Challenges in Deploying Machine Learning: A Survey of Case Studies

Multi-Source Deep Transfer Neural Network Algorithm

Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation

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