Abadifard, Soheil2024-08-092024-08-092024-072024-072024-08-05https://hdl.handle.net/11693/115735Cataloged from PDF version of article.Thesis (Master's): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2024.Includes bibliographical references (leaves 56-66).The classification of imbalanced data streams, which have unequal class distributions, is a key difficulty in machine learning, especially when dealing with multiple classes and concept drift. While binary imbalanced data stream classification tasks have received considerable attention, only a few studies have focused on multi-class imbalanced data streams. Additionally, dealing with the dynamic imbalance ratio is of great importance. This study introduces a novel, robust, and resilient approach to address these challenges by integrating Locality Sensitive Hashing with Random Hyperplane Projections (LSH-RHP) into the Dynamic Ensemble Diversification (DynED) framework. To the best of our knowledge, we present the first application of LSH-RHP for undersampling in the context of imbalanced non-stationary data streams. The proposed method, undersamples majority classes by utilizing LSH-RHP, provides a balanced training set, and improves the ensemble’s prediction accuracy. We conduct comprehensive experiments on 23 real-world and ten semi-synthetic datasets and compare LSHDynED with 15 state-of-the-art methods. The results reveal that LSH-DynED outperforms other approaches in terms of both Kappa and mG-Mean effectiveness measures, demonstrating its capability in dealing with multi-class imbalanced non-stationary data streams. Notably, LSH-DynED performs well in large-scale, high-dimensional datasets with considerable class imbalances and demonstrates adaptation and robustness in real-world circumstances. For the reproducibility of our results, we have made our implementation available on GitHub.xii, 67 leaves : illustrations, charts ; 30 cm.Englishinfo:eu-repo/semantics/openAccessData streamClassificationClass imbalanceConcept driftEnsemble learningLocality sensitive hashingThesisB151424