Results depict that DDCS not only outperforms the state-of-art ensemble methods for data stream classification in terms of accuracy but is also significantly more efficient in terms of processing time and memory consumption.
In this paper, we propose a novel dynamic classifier selection framework for data streams called Double Dynamic Classifier Selection (DDCS). We compare DDCS ...
Nov 27, 2022 · Dynamic ensemble selection is the selection of the best classifier on each test set, and the region where the instances of classifiers are ...
However, these are roughly overlooked in data stream mining. In this paper, we propose a novel dynamic classifier selection framework for data streams called ...
The proposed method offers an effective solution for achieving an accurate and resilient classification in the context of imbalanced multiclass drifted data ...
Sep 17, 2023 · Abstract:Modern streaming data categorization faces significant challenges from concept drift and class imbalanced data.
Oct 21, 2023 · Ensemble methods are commonly used in classification due to their remarkable performance. Achieving high accuracy in a data stream ...
This article proposes a method for generating data streams with given parameters based on real-world static data. The method uses one-dimensional interpolation ...
Jun 22, 2022 · Ensemble learning, as a popular method to tackle concept drift in data stream, forms a combination of base classifiers according to their global ...
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We propose a novel framework employing stratified bagging for training base classifiers to integrate data preprocessing and dynamic ensemble selection methods ...
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