The effect of pruning on the efficiency and effectiveness of hybrid imbalanced multiclass classification models
Year: 2025
Type: article
Abstract: Hybrid models are recognized as one of the most effective approaches to address the imbalanced data problem. In these models, data-level methods such as over-sampling are combined with algorithm-level methods, such as ensemble approaches. However, the resulting models can face challenges concerning inefficiency and ineffectiveness. A solution to tackle these issues is proposed in this paper, which includes a novel weighted F1-ordered pruning technique integrated with two state-of-the-art hybrid models, Balanced Bagging and Balanced One-versus-One. Unlike prior hybrid models designed primarily to address the binary imbalance problem, the proposed approach is specifically designed to tackle the challenging multi-class classification imbalance problem. An extensive experimental evaluation and statistical validation were conducted, and demonstrated that the Pruned Balanced Bagging ensemble remarkably outperforms the considered hybrid models.
Source: Array
Institution: Hashemite University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,680
OpenAlex ID: W4416768505