An Overview of Concept Drift Applications

Žliobaitė, Indrė; Pechenizkiy, Mykola; Gama, João

doi:10.1007/978-3-319-26989-4_4

Cited by 261 publications

(188 citation statements)

References 70 publications

(82 reference statements)

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“…In many data mining applications, e.g., in sensor networks, banking, energy management, or telecommunication, the need for processing rapid data streams is becoming more and more common [50]. Such demands have led to the development of classification algorithms that are capable of processing instances one by one, while using limited memory and time.…”

Section: Introductionmentioning

confidence: 99%

Prequential AUC: properties of the area under the ROC curve for data streams with concept drift

2017

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Modern data-driven systems often require classifiers capable of dealing with streaming imbalanced data and concept changes. The assessment of learning algorithms in such scenarios is still a challenge, as existing online evaluation measures focus on efficiency, but are susceptible to class ratio changes over time. In case of static data, the area under the receiver operating characteristics curve, or simply AUC, is a popular measure for evaluating classifiers both on balanced and imbalanced class distributions. However, the characteristics of AUC calculated on time-changing data streams have not been studied. This paper analyzes the properties of our recent proposal, an incremental algorithm that uses a sorted tree structure with a sliding window to compute AUC with forgetting. The resulting evaluation measure, called prequential AUC, is studied in terms of: visualization over time, processing speed, differences compared to AUC calculated on blocks of examples, and consistency with AUC calculated traditionally. Simulation results show that the proposed measure is statistically consistent with AUC computed traditionally on streams without drift and comparably fast to existing evaluation procedures. Finally, experiments on real-world and synthetic data showcase characteristic properties of prequential AUC compared to classification accuracy, G-mean, Kappa, Kappa M, and recall when used to evaluate classifiers on imbalanced streams with various difficulty factors.

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Section: Introductionmentioning

confidence: 99%

Prequential AUC: properties of the area under the ROC curve for data streams with concept drift

2017

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“…We can find many examples of real-world SL applications [32], such as mobile phones, industrial process controls, intelligent user interfaces, intrusion detection, spam detection, fraud detection, loan recommendation, monitoring and traffic management, among others [33]. In this context, the Internet of Things (IoT) has become one of the main applications of SL [34], since it is producing huge quantity of data continuously in real-time.…”

Section: Stream Learning In the Big Data Eramentioning

confidence: 99%

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

et al. 2020

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The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.

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“…Algorithms working at the data generating side must take these constraints into account. Also the underlying data distribution may change which is known as concept drift [117]. For instance, due to wear, the accuracy of sensors may decrease.…”

Section: Algorithmic Challengesmentioning

confidence: 99%

The Internet of Things

Stolpe

2016

SIGKDD Explor. Newsl.

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Nowadays, data is created by humans as well as automatically collected by physical things, which embed electronics, software, sensors and network connectivity. Together, these entities constitute the Internet of Things (IoT). The automated analysis of its data can provide insights into previously unknown relationships between things, their environment and their users, facilitating an optimization of their behavior. Especially the real-time analysis of data, embedded into physical systems, can enable new forms of autonomous control. These in turn may lead to more sustainable applications, reducing waste and saving resources. IoT's distributed and dynamic nature, resource constraints of sensors and embedded devices as well as the amounts of generated data are challenging even the most advanced automated data analysis methods known today. In particular, the IoT requires a new generation of distributed analysis methods. Many existing surveys have strongly focused on the centralization of data in the cloud and big data analysis, which follows the paradigm of parallel high-performance computing. However, bandwidth and energy can be too limited for the transmission of raw data, or it is prohibited due to privacy constraints. Such communication-constrained scenarios require decentralized analysis algorithms which at least partly work directly on the generating devices. After listing data-driven IoT applications, in contrast to existing surveys, we highlight the differences between cloudbased and decentralized analysis from an algorithmic perspective. We present the opportunities and challenges of research on communication-efficient decentralized analysis algorithms. Here, the focus is on the difficult scenario of vertically partitioned data, which covers common IoT use cases. The comprehensive bibliography aims at providing readers with a good starting point for their own work.

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An Overview of Concept Drift Applications

Cited by 261 publications

References 70 publications

Prequential AUC: properties of the area under the ROC curve for data streams with concept drift

Prequential AUC: properties of the area under the ROC curve for data streams with concept drift

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

The Internet of Things

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