Active Learning with Abstaining Classifiers for Imbalanced Drifting Data Streams

Korycki, Łukasz; Cano, Alberto; Krawczyk, Bartosz

doi:10.1109/bigdata47090.2019.9006453

Cited by 29 publications

(12 citation statements)

References 53 publications

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“…Data stream mining can detect changes in the property of the stream data and adapt the classification model accordingly. However, there are still too may open issues both from the basic research and application perspectives [32][33][34][35][36] which call for the scientific community to propose new efficient and effective solutions, particularly using high-performance computing architectures.…”

Section: Discussionmentioning

confidence: 99%

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Introductory Chapter: Data Streams and Online Learning in Social Media

Cano¹

2020

Social Media and Machine Learning

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Section: Discussionmentioning

confidence: 99%

“…In such a scenario, there are other solutions much more efficient for real-time streams. Apache Spark Streaming, Apache Flink, and Apache Storm are MapReduce-based frameworks for streaming data [28][29][30][31][32]. However, they lack efficient implementations of effective machine learning algorithms.…”

Section: Data Stream Mining For Online Learningmentioning

confidence: 99%

Introductory Chapter: Data Streams and Online Learning in Social Media

Cano¹

2020

Social Media and Machine Learning

Self Cite

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“…They are usually selected to offer new information to the classifier, instead of reinforcing old concepts [31]. Although there is a good amount of research on active learning for static scenarios [35], there exist but a few solutions that take into account the drifting and evolving nature of streams [22,41]. Another branch of works focuses on semisupervised learning [32], usually using clustering-based solutions [28].…”

Section: Labeling Constraints In Data Streamsmentioning

confidence: 99%

Mining Drifting Data Streams on a Budget: Combining Active Learning with Self-Labeling

Korycki¹,

Krawczyk²

2021

Preprint

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Mining data streams poses a number of challenges, including the continuous and non-stationary nature of data, the massive volume of information to be processed and constraints put on the computational resources. While there is a number of supervised solutions proposed for this problem in the literature, most of them assume that access to the ground truth (in form of class labels) is unlimited and such information can be instantly utilized when updating the learning system. This is far from being realistic, as one must consider the underlying cost of acquiring labels. Therefore, solutions that can reduce the requirements for ground truth in streaming scenarios are required. In this paper, we propose a novel framework for mining drifting data streams on a budget, by combining information coming from active learning and self-labeling. We introduce several strategies that can take advantage of both intelligent instance selection and semi-supervised procedures, while taking into account the potential presence of concept drift. Such a hybrid approach allows for efficient exploration and exploitation of streaming data structures within realistic labeling budgets. Since our framework works as a wrapper, it may be applied with different learning algorithms. Experimental study, carried out on a diverse set of real-world data streams with various types of concept drift, proves the usefulness of the proposed strategies when dealing with highly limited access to class labels. The presented hybrid approach is especially feasible when one cannot increase a budget for labeling or replace an inefficient classifier. We deliver a set of recommendations regarding areas of applicability for our strategies.

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“…In [61], Khanchi et al proposed an active learning algorithm named StreamGP, which adapt over time as genetic programming (GP) individuals improve. Korycki et al [62] present an online framework called Active Learning Strategy (MD-OAL) for imbalanced data streams with partially labeled.…”

Section: Learning From Class Imbalance Data Streammentioning

confidence: 99%

Two-Stage Cost-Sensitive Learning for Data Streams With Concept Drift and Class Imbalance

Sun

Sun²,

Dai

2020

IEEE Access

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Most methods for classifying data streams operate under the hypothesis that the distribution of classes is balanced. Unfortunately, the phenomenon of class imbalance widely exists in many real-world applications. In addition, the underlying concept of data stream may change in a certain way over time, and attacks increase the difficulty of data stream mining. Motivated by this challenge, a Two-Stage Cost-Sensitive (TSCS) classification is proposed for addressing the class imbalance issue in non-stationary data streams. We propose a novel two-stage cost-sensitive framework for data stream classification by utilizing cost information in both feature selection stage and classification stage. Moreover, a window adaptation and drift detection mechanism, which guarantees that an ensemble can adapt promptly to concept drift, is embedded in our method. Our algorithm is compared with competitive algorithms on different kinds of datasets. The result demonstrates that TSCS obtains significant improvement in terms of class imbalance data stream metrics.

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Active Learning with Abstaining Classifiers for Imbalanced Drifting Data Streams

Cited by 29 publications

References 53 publications

Introductory Chapter: Data Streams and Online Learning in Social Media

Introductory Chapter: Data Streams and Online Learning in Social Media

Mining Drifting Data Streams on a Budget: Combining Active Learning with Self-Labeling

Two-Stage Cost-Sensitive Learning for Data Streams With Concept Drift and Class Imbalance

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