Dynamic Ensemble Diversification and Hash-Based Undersampling for the Classification of Multi-Class Imbalanced Data Streams<\/p>\n
Soheil Abadifard
\nMaster Student<\/p>\n
(Supervisor: Prof.Dr. Fazl\u0131 Can)
\nComputer Engineering Department
\nBilkent University<\/p>\n
Abstract: 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\u2019s prediction accuracy. We conduct comprehensive experiments on 23 real-world and ten semi-synthetic datasets and compare LSH-DynED 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.<\/p>\n
DATE: July 29, Monday @ 13:30 Place: EA 409<\/p>\n","protected":false},"excerpt":{"rendered":"
Dynamic Ensemble Diversification and Hash-Based Undersampling for the Classification of Multi-Class Imbalanced Data Streams Soheil Abadifard Master Student (Supervisor: Prof.Dr. Fazl\u0131 Can) Computer Engineering Department Bilkent University Abstract: The classification of imbalanced data streams, which have unequal class distributions, is a key difficulty in machine learning, especially when dealing with [\u2026] <\/p>\n