Entropy-based dynamic ensemble classication algorithm for imbalanced data stream with concept drift.

ABSTRACT: Online imbalanced learning is an emerging topic that combines the challenges of class imbalance and concept drift. However, current works account for issues of class imbalance and concept drift. And only few works have considered these issues simultaneously. To this end, this paper proposes an entropy-based dynamic ensemble classification algorithm (EDAC) to consider data streams with class imbalance and concept drift simultaneously. First, to address the problem of imbalanced learning in training data chunks arriving at different times, EDAC adopts an entropy-based balanced strategy. It divides the data chunks into multiple balanced sample pairs based on the differences in the information entropy between classes in the sample data chunk. Additionally, we propose a density-based sampling m

SUBMITTER: Gong J 

PROVIDER: S-EPMC11643253 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature