Exemplary accuracy for data stream with abrupt concept. Red line denotes drift detection, green stabilization detection, and blue beginning of a real drift.

Exemplary accuracy for data stream with abrupt concept. Red line denotes drift detection, green stabilization detection, and blue beginning of a real drift.

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Modern analytical systems must process streaming data and correctly respond to data distribution changes. The phenomenon of changes in data distributions is called concept drift , and it may harm the quality of the used models. Additionally, the possibility of concept drift appearance causes that the used algorithms must be ready for the continuous...

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... V ar(W ) is a variance of scores obtained for the last K chunks. Sample data stream with detected drift and stabilization is presented in Fig. 2. The primary assumption of the proposed method is a faster model adaptation caused by the increased number of updates after a concept drift. This strategy allows for using the larger chunk sizes when the data is not changing. It also reduces the computational costs of retraining models. Alg. 1 present the whole procedure. Our method ...

Citations

... Implicit drift detection methods assume that the classifier is capable of self-adjusting to new instances coming from the stream while forgetting the old information (Liu et al., 2016). This way, new information is constantly incorporated into the learner, which should allow for adapting to evolving concepts (Kozal et al., 2021). Drawbacks of implicit methods lie in their parametrization -establishing proper learning and forgetting rates, as well as the size of a sliding window. ...
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Data streams are potentially unbounded sequences of instances arriving over time to a classifier. Designing algorithms that are capable of dealing with massive, rapidly arriving information is one of the most dynamically developing areas of machine learning. Such learners must be able to deal with a phenomenon known as concept drift, where the data stream may be subject to various changes in its characteristics over time. Furthermore, distributions of classes may evolve over time, leading to a highly difficult non-stationary class imbalance. In this work we introduce Robust Online Self-Adjusting Ensemble (ROSE), a novel online ensemble classifier capable of dealing with all of the mentioned challenges. The main features of ROSE are: (i) online training of base classifiers on variable size random subsets of features; (ii) online detection of concept drift and creation of a background ensemble for faster adaptation to changes; (iii) sliding window per class to create skew-insensitive classifiers regardless of the current imbalance ratio; and (iv) self-adjusting bagging to enhance the exposure of difficult instances from minority classes. The interplay among these features leads to an improved performance in various data stream mining benchmarks. An extensive experimental study comparing with 30 ensemble classifiers shows that ROSE is a robust and well-rounded classifier for drifting imbalanced data streams, especially under the presence of noise and class imbalance drift, while maintaining competitive time complexity and memory consumption. Results are supported by a thorough non-parametric statistical analysis.