Work

Adaptive Ensemble Framework With Synthetic Sampling for Tackling Class Imbalance Problem

Year: 2025

Type: article

Abstract: ABSTRACT Class imbalance is a critical challenge in heart disease prediction datasets, often leading to biased models with poor performance on the minority class. This paper proposes a novel algorithm, the Adaptive Synthetic Ensemble Balancer (ASEB), designed to address this issue through a combination of advanced synthetic data generation, adaptive class weighting, and ensemble learning techniques. ASEB integrates Adaptive Synthetic Sampling (ADASYN) and Generative Adversarial Networks (GANs) for synthetic sample generation, dynamic class weighting, and ensemble learning strategies. Experimental results demonstrate ASEB's effectiveness in improving predictive accuracy and robustness in imbalanced heart disease datasets.

Source: Engineering Reports

DOI: 10.1002/eng2.70109

Authors: R. Sasirekha, B. Kanisha

Institution: SRM Institute of Science and Technology

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,270