Semi-supervised classification on data streams with recurring concept drift and concept evolution

Xiu-lin, Zheng; Li, Peipei; Hu, Xuegang; Yu, Ke

doi:10.1016/j.knosys.2021.106749

Cited by 47 publications

(16 citation statements)

References 42 publications

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“…Many methods can then deploy this phase (e.g., K-means [7] and Kolmogorov-Smirnov (KS) statistics test [9]). The choice may depend on the learning setting, i.e., unsupervised, supervised, [6], or semisupervised [10]. The hypothesis test calculator analyzes and questions the accuracy of the report sent by the testing calculator.…”

Section: A Concept-drift Detectionmentioning

confidence: 99%

AI/ML Service Enablers and Model Maintenance for Beyond 5G Networks

Samdanis¹,

Abbou²,

Song³

et al. 2023

IEEE Network

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Intelligence and Machine Learning (AI/ML) can transform mobile communications, enable new applications and services, and pave the way beyond 5G. The adoption of AI/ML may also advance network optimizations and service life-cycle management. This article provides an overview of AI/ML analytics exploring the main concepts in relation to network automation and service-based architectures. Furthermore, it sheds light on AI/ML analytics service enablers by considering a system-level approach elaborating on the key technologies related to service discovery, request, control, and reporting of AI/ML analytics. Finally, it provides an analysis for maintaining the services of AI/ML analytics up-to-date, by considering modifications of the AI/ML model in order to detect, interpret, and compensate for potential performance drifts.

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Section: A Concept-drift Detectionmentioning

confidence: 99%

AI/ML Service Enablers and Model Maintenance for Beyond 5G Networks

Samdanis¹,

Abbou²,

Song³

et al. 2023

IEEE Network

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“…These weights indicate the best corresponding model for the current concept [29]. Ensemblebased approaches dealing with concept drift are categorized into homogeneous and heterogeneous.…”

Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Model to Minimize Fluctuation Influences on Short-Term Medical Workload Prediction

Mizan,

Taghipour

2022

Scientia Iranica

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Real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. The erroneous data causes inaccurate forecasting which leads to business loss. Moreover, the concept drift problem is a known problem in time series forecasting that also results in poor forecasting accuracy. The execution time of a machine learning model is also crucial when it is deployed in a real-time environment. This work presents an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the timevariant windowing technique. A data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. A meta-model is developed in the online forecasting phase which ensures faster execution for incoming data. The model is implemented for the medical workload prediction after testing and comparing with a few other heterogeneous ensemble models. The comparison results show in terms of the root mean squared error, the proposed model performs at least 65.7% better than the heterogeneous stacked ensemble models on the experimental dataset. Moreover, in comparison to the other standalone models considered in this experiment, the ABRE model reduces the prediction error by approximately 73.6%.

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“…Hence, when a concept drift happens, the new rumour subgraphs would no longer be generated according to the old stationary distribution. Then, the change in the distributions can be identified using the CUSUM test, see [47], which is based on the Central Limit Theorem for multivariate vector streams.…”

Section: Dealing With Concept Driftmentioning

confidence: 99%

Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs

Nguyên¹,

Phan²,

Nguyen³

et al. 2022

Preprint

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The propagation of rumours on social media poses an important threat to societies, so that various techniques for rumour detection have been proposed recently. Yet, existing work focuses on what entities constitute a rumour, but provides little support to understand why the entities have been classified as such. This prevents an effective evaluation of the detected rumours as well as the design of countermeasures. In this work, we argue that explanations for detected rumours may be given in terms of examples of related rumours detected in the past. A diverse set of similar rumours helps users to generalize, i.e., to understand the properties that govern the detection of rumours. Since the spread of rumours in social media is commonly modelled using feature-annotated graphs, we propose a query-by-example approach that, given a rumour graph, extracts the k most similar and diverse subgraphs from past rumours. The challenge is that all of the computations require fast assessment of similarities between graphs. To achieve an efficient and adaptive realization of the approach in a streaming setting, we present a novel graph representation learning technique and report on implementation considerations. Our evaluation experiments show that our approach outperforms baseline techniques in delivering meaningful explanations for various rumour propagation behaviours.

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Semi-supervised classification on data streams with recurring concept drift and concept evolution

Cited by 47 publications

References 42 publications

AI/ML Service Enablers and Model Maintenance for Beyond 5G Networks

AI/ML Service Enablers and Model Maintenance for Beyond 5G Networks

An Ensemble Model to Minimize Fluctuation Influences on Short-Term Medical Workload Prediction

Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs

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