Chunk-Adaptive Restoration visualization. Red line marks the concept drit, green line marks the stabilization.

Chunk-Adaptive Restoration visualization. Red line marks the concept drit, green line marks the stabilization.

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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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... a fixed data chunk size, which is a parameter of these algorithms. Our proposal does not modify the core of a learning algorithm itself. Still, based on the predictive performance estimated on a given data chunk, it only indicates what data chunk size is to be taken by a given algorithm in the next step. We provide the schema of our method in Fig. 1. The intuition tells us that after the occurrence of the concept drit, the size of the chunk should be small to quickly train new models that will replace the models learned on the data from the previous concept in the ensemble. When the stabilization is reached, the ensemble contains base models trained on data from a new concept. At ...

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... 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.