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Classifier Pool Generation based on a Two-level Diversity Approach., , , , and . ICPR, page 2414-2421. IEEE, (2020)On Dynamic Feature Weighting for Feature Drifting Data Streams., , , , and . ECML/PKDD (2), volume 9852 of Lecture Notes in Computer Science, page 129-144. Springer, (2016)Overcoming feature drifts via dynamic feature weighted k-nearest neighbor learning., , , , and . ICPR, page 2186-2191. IEEE, (2016)ADADRIFT: An Adaptive Learning Technique for Long-history Stream-based Recommender Systems., , and . SMC, page 2593-2600. IEEE, (2020)Univariate Time Series Prediction using Data Stream Mining Algorithms and Temporal Dependence., , and . ICAART (2), page 398-408. SCITEPRESS, (2022)Dynamically Selected Ensemble for Data Stream Classification., , , and . IJCNN, page 1-7. IEEE, (2021)Decision tree-based feature ranking in concept drifting data streams., , and . SAC, page 590-592. ACM, (2019)SFNClassifier: a scale-free social network method to handle concept drift., , and . SAC, page 786-791. ACM, (2014)Towards the Overcome of Performance Pitfalls in Data Stream Mining Tools., , and . IJCNN, page 1-6. IEEE, (2021)Random Forest Dissimilarity for High-Dimension Low Sample Size Classification., , , and . CoRR, (2023)