No Free Lunch Theorem for concept drift detection in streaming data classification: A review

Hu, Hanqing; Kantardzic, Mehmed; Sethi, Tegjyot Singh

doi:10.1002/widm.1327

Cited by 65 publications

(33 citation statements)

References 96 publications

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“…The successful design of an effective detector is not straightforward, yet it is primordial to achieve a more reliable system. The way to find the best strategy for concept drift detection still remains an open research issue, as confirmed in [36]. This challenge to find the best universal solution becomes evident in the most recent comparison among drift detectors carried out by [37].…”

Section: Concept Drift Detectionmentioning

confidence: 99%

“…Consequently, drift detection turns into a relevant factor for those active mechanisms that need a triggering mechanism to perform an adaptation after drift occurs [36]. A drift detector should estimate the time instant at which change occurs over the data stream so that when the detection appears, the adaptation mechanism is applied to the base learner in order to avoid the degradation of its predictive performance.…”

Section: Concept Drift Detectionmentioning

confidence: 99%

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Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum

et al. 2021

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The current IT market is more and more dominated by the “cloud continuum”. In the “traditional” cloud, computing resources are typically homogeneous in order to facilitate economies of scale. In contrast, in edge computing, computational resources are widely diverse, commonly with scarce capacities and must be managed very efficiently due to battery constraints or other limitations. A combination of resources and services at the edge (edge computing), in the core (cloud computing), and along the data path (fog computing) is needed through a trusted cloud continuum. This requires novel solutions for the creation, optimization, management, and automatic operation of such infrastructure through new approaches such as infrastructure as code (IaC). In this paper, we analyze how artificial intelligence (AI)-based techniques and tools can enhance the operation of complex applications to support the broad and multi-stage heterogeneity of the infrastructural layer in the “computing continuum” through the enhancement of IaC optimization, IaC self-learning, and IaC self-healing. To this extent, the presented work proposes a set of tools, methods, and techniques for applications’ operators to seamlessly select, combine, configure, and adapt computation resources all along the data path and support the complete service lifecycle covering: (1) optimized distributed application deployment over heterogeneous computing resources; (2) monitoring of execution platforms in real time including continuous control and trust of the infrastructural services; (3) application deployment and adaptation while optimizing the execution; and (4) application self-recovery to avoid compromising situations that may lead to an unexpected failure.

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Section: Concept Drift Detectionmentioning

confidence: 99%

Section: Concept Drift Detectionmentioning

confidence: 99%

Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum

et al. 2021

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“…The generation mechanism also allows easy alteration of dataset dimensionality. To demonstrate the ability of HRDD to handle high-dimensional data, we considered d = [5,10,15,20,30,40]. Data generation details can be found in Table 3.…”

Section: Experiments 1: Understanding Hrddmentioning

confidence: 99%

“…Various detection methods have been proposed to explicitly mark out the drifts [4], [5]. Nonetheless,…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Reduced-space Drift Detection Framework for Multivariate Supervised Data Streams

Zhang¹,

Tiňo²,

Yao³

2021

IEEE Trans. Knowl. Data Eng.

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In a streaming environment, the characteristics of the data themselves and their relationship with the labels may change over time. Most drift detection methods for supervised data streams are performance-based, that is, they detect changes only after the classification accuracy deteriorates. This may not be sufficient in many application areas where the reason behind a drift is also important. Another category of drift detectors are data distribution-based detectors. Although they can detect some drifts within the input space, changes affecting only the labelling mechanism cannot be identified. Furthermore, little work is available on drift detection for high-dimensional data streams. In this paper we propose an advanced Hierarchical Reduced-space Drift Detection (HRDD) framework for supervised data streams which captures drifts regardless of their effects on classification performance. This framework suggests monitoring both marginal and class-conditional distributions within a lower-dimensional space specifically relevant to the assigned classification task. Experimental comparisons have demonstrated that HRDD not only achieves high-quality performance on high-dimensional data streams, but also outperforms its competitors in terms of detection recall, precision and F-measure across a wide range of different concept drift types including subtle drifts.

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“…Numerous algorithms are proposed for managing concept drift. [82,154] review the generic algorithms to handle the concept drift. There are two main detection methods, performance-based and data distribution-based.…”

Section: Data Collectionmentioning

confidence: 99%

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications

Qian

Wen

et al. 2020

ACM Comput. Surv.

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Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This paper provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy, and subsequently assess and compare existing standard techniques used at individual stages.

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No Free Lunch Theorem for concept drift detection in streaming data classification: A review

Cited by 65 publications

References 96 publications

Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum

Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum

Hierarchical Reduced-space Drift Detection Framework for Multivariate Supervised Data Streams

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications

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